# Copyright 2024 Bytedance Ltd. and/or its affiliates
# Copyright 2023-2024 SGLang Team
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


import logging
import os
import warnings

import psutil
import torch
import torch.distributed
from codetiming import Timer
from omegaconf import open_dict
from torch.distributed.device_mesh import init_device_mesh

import verl.utils.torch_functional as verl_F
from verl import DataProto
from verl.single_controller.base import Worker
from verl.single_controller.base.decorator import Dispatch, register
from verl.utils import hf_tokenizer
from verl.utils.checkpoint.fsdp_checkpoint_manager import FSDPCheckpointManager
from verl.utils.device import get_device_id, get_device_name, get_nccl_backend, get_torch_device
from verl.utils.flops_counter import FlopsCounter
from verl.utils.fs import copy_to_local
from verl.utils.fsdp_utils import (
    get_fsdp_wrap_policy,
    get_init_weight_context_manager,
    init_fn,
    load_fsdp_model_to_gpu,
    load_fsdp_optimizer,
    offload_fsdp_model_to_cpu,
    offload_fsdp_optimizer,
)
from verl.utils.import_utils import import_external_libs
from verl.utils.model import compute_position_id_with_mask
from verl.utils.profiler import log_gpu_memory_usage
from verl.workers.fsdp_workers import ActorRolloutRefWorker
from verl.workers.sharding_manager.fsdp_ulysses import FSDPUlyssesShardingManager

logger = logging.getLogger(__file__)
logger.setLevel(os.getenv("VERL_PPO_LOGGING_LEVEL", "WARN"))


def create_device_mesh(world_size, fsdp_size):
    if fsdp_size < 0 or fsdp_size >= world_size:
        device_mesh = init_device_mesh(get_device_name(), mesh_shape=(world_size,), mesh_dim_names=["fsdp"])
    else:
        device_mesh = init_device_mesh(
            get_device_name(), mesh_shape=(world_size // fsdp_size, fsdp_size), mesh_dim_names=["ddp", "fsdp"]
        )
    return device_mesh


def get_sharding_strategy(device_mesh):
    from torch.distributed.fsdp import ShardingStrategy

    if device_mesh.ndim == 1:
        sharding_strategy = ShardingStrategy.FULL_SHARD
    elif device_mesh.ndim == 2:
        sharding_strategy = ShardingStrategy.HYBRID_SHARD
    else:
        raise NotImplementedError(f"Get device mesh ndim={device_mesh.ndim}, but only support 1 or 2")
    return sharding_strategy


class SPINRolloutRefWorker(ActorRolloutRefWorker):
    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
    def init_model(self):
        from recipe.spin.dp_actor import SPINDataParallelPPOActor as DataParallelPPOActor

        # This is used to import external_lib into the huggingface systems
        import_external_libs(self.config.model.get("external_lib", None))

        from omegaconf import OmegaConf

        override_model_config = OmegaConf.to_container(self.config.model.get("override_config", OmegaConf.create()))

        use_remove_padding = self.config.model.get("use_remove_padding", False)
        use_fused_kernels = self.config.model.get("use_fused_kernels", False)

        if self._is_actor or self._is_rollout or self._is_ref:
            # we need the model for actor and rollout
            if self._is_actor or self._is_ref:
                optim_config = self.config.actor.optim
                fsdp_config = self.config.actor.fsdp_config
            else:
                optim_config = None
                fsdp_config = OmegaConf.create()
            self.actor_module_fsdp, self.actor_optimizer, self.actor_lr_scheduler, self.actor_model_config = (
                self._build_model_optimizer(
                    model_path=self.config.model.path,
                    fsdp_config=fsdp_config,
                    optim_config=optim_config,
                    override_model_config=override_model_config,
                    use_remove_padding=use_remove_padding,
                    use_fused_kernels=use_fused_kernels,
                    enable_gradient_checkpointing=self.config.model.get("enable_gradient_checkpointing", False),
                    trust_remote_code=self.config.model.get("trust_remote_code", False),
                    use_liger=self.config.model.get("use_liger", False),
                    role="actor",
                )
            )

            # get the original unwrapped module
            self.actor_module = self.actor_module_fsdp._fsdp_wrapped_module

            if self._is_offload_optimizer:
                offload_fsdp_optimizer(optimizer=self.actor_optimizer)
                log_gpu_memory_usage("After offload actor optimizer during init", logger=logger)
        # load from checkpoint
        if self._is_actor or self._is_ref:
            OmegaConf.set_struct(self.config.actor, True)
            with open_dict(self.config.actor):
                self.config.actor.use_remove_padding = use_remove_padding
                self.config.actor.use_fused_kernels = use_fused_kernels
            self.actor = DataParallelPPOActor(
                config=self.config.actor, actor_module=self.actor_module_fsdp, actor_optimizer=self.actor_optimizer
            )

        if self._is_rollout:
            self.rollout, self.rollout_sharding_manager = self._build_rollout(
                trust_remote_code=self.config.model.get("trust_remote_code", False)
            )

        if self._is_ref:
            self.ref_module_fsdp = self._build_model_optimizer(
                model_path=self.config.model.path,
                fsdp_config=self.config.ref.fsdp_config,
                optim_config=None,
                override_model_config=override_model_config,
                use_remove_padding=use_remove_padding,
                use_fused_kernels=use_fused_kernels,
                trust_remote_code=self.config.model.get("trust_remote_code", False),
                use_liger=self.config.model.get("use_liger", False),
                role="ref",
            )[0]
            OmegaConf.set_struct(self.config.ref, True)
            with open_dict(self.config.ref):
                self.config.ref.use_remove_padding = use_remove_padding
                self.config.ref.use_fused_kernels = use_fused_kernels
            self.ref_policy = DataParallelPPOActor(config=self.config.ref, actor_module=self.ref_module_fsdp)
            self.checkpoint_manager = FSDPCheckpointManager(
                model=self.actor_module_fsdp,
                optimizer=self.actor.actor_optimizer,
                lr_scheduler=self.actor_lr_scheduler,
                processing_class=self.processor if self.processor is not None else self.tokenizer,
                checkpoint_config=self.config.actor.checkpoint,
            )

        if self._is_actor:
            self.flops_counter = FlopsCounter(self.actor_model_config)
            self.checkpoint_manager = FSDPCheckpointManager(
                model=self.actor_module_fsdp,
                optimizer=self.actor.actor_optimizer,
                lr_scheduler=self.actor_lr_scheduler,
                processing_class=self.processor if self.processor is not None else self.tokenizer,
                checkpoint_config=self.config.actor.checkpoint,
            )

    @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO)
    def compute_ref_log_prob(self, data: DataProto):
        assert self._is_ref

        # Support all hardwares
        data = data.to(get_device_id())

        micro_batch_size = self.config.ref.log_prob_micro_batch_size_per_gpu
        data.meta_info["micro_batch_size"] = micro_batch_size
        data.meta_info["temperature"] = self.config.rollout.temperature
        data.meta_info["max_token_len"] = self.config.ref.log_prob_max_token_len_per_gpu
        data.meta_info["use_dynamic_bsz"] = self.config.ref.log_prob_use_dynamic_bsz
        with self.ulysses_sharding_manager:
            data = self.ulysses_sharding_manager.preprocess_data(data)
            output = self.ref_policy.compute_log_prob(data=data)
            output = DataProto.from_dict(tensors={"ref_log_prob": output})
            output = self.ulysses_sharding_manager.postprocess_data(output)

        output = output.to("cpu")

        # https://pytorch.org/docs/stable/notes/fsdp.html#fsdp-notes
        # unshard the root FSDP module
        if self.world_size > 1:
            self.ref_policy.actor_module._handle.reshard(True)

        return output

    @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO)
    def compute_log_prob(self, data: DataProto):
        assert self._is_actor
        if self._is_offload_param:
            load_fsdp_model_to_gpu(self.actor_module_fsdp)

        # Support all hardwares
        data = data.to(get_device_id())
        # we should always recompute old_log_probs when it is HybridEngine
        data.meta_info["micro_batch_size"] = self.config.rollout.log_prob_micro_batch_size_per_gpu
        data.meta_info["max_token_len"] = self.config.rollout.log_prob_max_token_len_per_gpu
        data.meta_info["use_dynamic_bsz"] = self.config.rollout.log_prob_use_dynamic_bsz
        data.meta_info["temperature"] = self.config.rollout.temperature
        # perform recompute log_prob
        with self.ulysses_sharding_manager:
            data = self.ulysses_sharding_manager.preprocess_data(data)
            output = self.actor.compute_log_prob(data=data)
            output = DataProto.from_dict(
                tensors={"old_log_probs": output}, meta_info={"temperature": self.config.rollout.temperature}
            )
            output = self.ulysses_sharding_manager.postprocess_data(output)

        output = output.to("cpu")

        # https://pytorch.org/docs/stable/notes/fsdp.html#fsdp-notes
        # unshard the root FSDP module
        if self.world_size > 1:
            self.actor.actor_module._handle.reshard(True)

        if self._is_offload_param:
            offload_fsdp_model_to_cpu(self.actor_module_fsdp)

        log_gpu_memory_usage("After compute_log_prob", logger=logger)
        return output

    @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO)
    def update_actor_dpo(self, data: DataProto):
        """
        Wrapper for actor update step. Handles FSDP state management.
        Calls self.actor.update_policy which now contains DPO logic based
        on pre-calculated log probabilities.
        """
        # Support all hardwares
        data = data.to(get_device_id())

        assert self._is_actor  # Make sure this worker has the actor role
        if self.actor is None:
            raise RuntimeError("Actor instance (self.actor) not initialized in worker.")

        # --- FSDP State Management ---
        if self._is_offload_param:
            load_fsdp_model_to_gpu(self.actor_module_fsdp)
        if self._is_offload_optimizer:
            load_fsdp_optimizer(optimizer=self.actor_optimizer, device_id=get_device_id())

        log_gpu_memory_usage("Before update policy (DPO via PPO path)", logger=logger)

        # --- Ulysses Sharding (if used) ---
        with self.ulysses_sharding_manager:
            data = self.ulysses_sharding_manager.preprocess_data(data=data)

            # --- Call the core update method (now containing DPO logic) ---
            with Timer(name="update_policy_dpo_via_ppo", logger=None) as timer:  # Use a distinct timer name
                # Calls the modified update_policy method
                metrics = self.actor.update_policy_dpo_with_ref(data=data)  # <-- THIS CALLS THE MODIFIED FUNCTION
            delta_time = timer.last

            # --- Add Performance Metrics ---
            # MFU calculation might be less accurate/meaningful here for DPO
            metrics["perf/approx_tokens_processed"] = torch.sum(
                data.batch.get("attention_mask", torch.tensor(0))
            ).item()  # Approx tokens
            metrics["perf/max_memory_allocated_gb"] = get_torch_device().max_memory_allocated() / (1024**3)
            metrics["perf/max_memory_reserved_gb"] = get_torch_device().max_memory_reserved() / (1024**3)
            metrics["perf/cpu_memory_used_gb"] = psutil.virtual_memory().used / (1024**3)
            global_num_tokens = data.meta_info["global_token_num"]
            estimated_flops, promised_flops = self.flops_counter.estimate_flops(global_num_tokens, delta_time)
            metrics["perf/mfu/actor"] = estimated_flops * self.config.ppo_epochs / promised_flops / self.world_size

            # --- LR Scheduler Step ---
            lr = self.actor_lr_scheduler.get_last_lr()[0]
            metrics["actor/lr"] = lr
            self.actor_lr_scheduler.step()

            log_gpu_memory_usage("After update policy (DPO via PPO path)", logger=logger)

            # --- Prepare Output ---
            output = DataProto(meta_info={"metrics": metrics})
            output = self.ulysses_sharding_manager.postprocess_data(data=output)
            output = output.to("cpu")

        # --- FSDP State Management (Offload) ---
        if self._is_offload_param:
            offload_fsdp_model_to_cpu(self.actor_module_fsdp)
        if self._is_offload_optimizer:
            offload_fsdp_optimizer(optimizer=self.actor_optimizer)

        return output


# TODO(sgm): we may need to extract it to dp_reward_model.py
class RewardModelWorker(Worker):
    """
    Note that we only implement the reward model that is subclass of AutoModelForTokenClassification.
    """

    def __init__(self, config):
        super().__init__()
        import torch.distributed

        if not torch.distributed.is_initialized():
            torch.distributed.init_process_group(backend=get_nccl_backend())
        self.config = config

        # build device mesh for Ulysses Sequence Parallel
        world_size = torch.distributed.get_world_size()
        from torch.distributed.device_mesh import init_device_mesh

        fsdp_size = self.config.model.fsdp_config.fsdp_size
        self.device_mesh = create_device_mesh(world_size=world_size, fsdp_size=fsdp_size)

        self.ulysses_device_mesh = None
        self.ulysses_sequence_parallel_size = self.config.get("ulysses_sequence_parallel_size", 1)
        dp = world_size // self.ulysses_sequence_parallel_size
        if self.ulysses_sequence_parallel_size > 1:
            self.ulysses_device_mesh = init_device_mesh(
                get_device_name(), mesh_shape=(dp, self.ulysses_sequence_parallel_size), mesh_dim_names=["dp", "sp"]
            )

        self.ulysses_sharding_manager = FSDPUlyssesShardingManager(self.ulysses_device_mesh)

        self.use_remove_padding = self.config.model.get("use_remove_padding", False)

        # normalize config
        if self.config.micro_batch_size is not None:
            self.config.micro_batch_size //= torch.distributed.get_world_size()
            self.config.micro_batch_size_per_gpu = self.config.micro_batch_size

    def _build_model(self, config):
        # the following line is necessary
        from torch.distributed.fsdp import CPUOffload
        from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
        from transformers import AutoConfig, AutoModelForTokenClassification

        # download the checkpoint from hdfs
        local_path = copy_to_local(config.model.path)

        if self.config.model.input_tokenizer is None:
            self._do_switch_chat_template = False
        else:
            self._do_switch_chat_template = True
            input_tokenizer_local_path = copy_to_local(config.model.input_tokenizer)
            self.input_tokenizer = hf_tokenizer(
                input_tokenizer_local_path, trust_remote_code=config.model.get("trust_remote_code", False)
            )
            self.tokenizer = hf_tokenizer(local_path, trust_remote_code=config.model.get("trust_remote_code", False))

        trust_remote_code = config.model.get("trust_remote_code", False)
        model_config = AutoConfig.from_pretrained(local_path, trust_remote_code=trust_remote_code)
        model_config.num_labels = 1

        # note that we have to create model in fp32. Otherwise, the optimizer is in bf16, which is incorrect
        init_context = get_init_weight_context_manager(
            use_meta_tensor=not model_config.tie_word_embeddings, mesh=self.device_mesh
        )

        with init_context(), warnings.catch_warnings():
            warnings.simplefilter("ignore")
            model_config.classifier_dropout = 0.0
            reward_module = AutoModelForTokenClassification.from_pretrained(
                pretrained_model_name_or_path=local_path,
                config=model_config,
                torch_dtype=torch.bfloat16,
                attn_implementation="flash_attention_2",
                trust_remote_code=trust_remote_code,
            )

            if config.model.get("use_remove_padding", False) or self.ulysses_sequence_parallel_size > 1:
                from verl.models.transformers.monkey_patch import apply_monkey_patch

                apply_monkey_patch(model=reward_module, ulysses_sp_size=self.ulysses_sequence_parallel_size)

            reward_module.to(torch.bfloat16)

        auto_wrap_policy = get_fsdp_wrap_policy(module=reward_module, config=self.config.model.fsdp_config)

        fsdp_mesh = self.device_mesh
        sharding_strategy = get_sharding_strategy(fsdp_mesh)

        reward_module = FSDP(
            reward_module,
            param_init_fn=init_fn,
            use_orig_params=False,
            auto_wrap_policy=auto_wrap_policy,
            device_id=get_device_id(),
            sharding_strategy=sharding_strategy,  # zero3
            sync_module_states=True,
            cpu_offload=CPUOffload(offload_params=True),
            forward_prefetch=False,
            device_mesh=self.device_mesh,
        )

        return reward_module

    @register(dispatch_mode=Dispatch.ONE_TO_ALL)
    def init_model(self):
        # This is used to import external_lib into the huggingface systems
        import_external_libs(self.config.model.get("external_lib", None))
        self.reward_module = self._build_model(config=self.config)

    def _forward_micro_batch(self, micro_batch):
        from flash_attn.bert_padding import index_first_axis, pad_input, rearrange, unpad_input

        from verl.utils.ulysses import gather_outputs_and_unpad, ulysses_pad_and_slice_inputs

        with torch.no_grad(), torch.autocast(device_type=get_device_name(), dtype=torch.bfloat16):
            input_ids = micro_batch["input_ids"]
            batch_size, seqlen = input_ids.shape
            attention_mask = micro_batch["attention_mask"]
            position_ids = micro_batch["position_ids"]

            if self.use_remove_padding:
                input_ids_rmpad, indices, *_ = unpad_input(
                    input_ids.unsqueeze(-1), attention_mask
                )  # input_ids_rmpad (total_nnz, ...)
                input_ids_rmpad = input_ids_rmpad.transpose(0, 1)  # (1, total_nnz)

                # unpad the position_ids to align the rotary
                position_ids_rmpad = index_first_axis(
                    rearrange(position_ids.unsqueeze(-1), "b s ... -> (b s) ..."), indices
                ).transpose(0, 1)

                # pad and slice the inputs if sp > 1
                if self.ulysses_sequence_parallel_size > 1:
                    input_ids_rmpad, position_ids_rmpad, pad_size = ulysses_pad_and_slice_inputs(
                        input_ids_rmpad, position_ids_rmpad, sp_size=self.ulysses_sequence_parallel_size
                    )

                # only pass input_ids and position_ids to enable flash_attn_varlen
                output = self.reward_module(
                    input_ids=input_ids_rmpad, attention_mask=None, position_ids=position_ids_rmpad, use_cache=False
                )  # prevent model thinks we are generating
                reward_rmpad = output.logits
                reward_rmpad = reward_rmpad.squeeze(0)  # (total_nnz)

                # gather output if sp > 1
                if self.ulysses_sequence_parallel_size > 1:
                    reward_rmpad = gather_outputs_and_unpad(
                        reward_rmpad, gather_dim=0, unpad_dim=0, padding_size=pad_size
                    )

                # pad it back
                rm_score = pad_input(reward_rmpad, indices=indices, batch=batch_size, seqlen=seqlen).squeeze(-1)
            else:
                output = self.reward_module(
                    input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, use_cache=False
                )
                rm_score = output.logits  # (batch_size, seq_len, 1)
                rm_score = rm_score.squeeze(-1)

            # extract the result of the last valid token
            eos_mask_idx = torch.argmax(position_ids * attention_mask, dim=-1)  # (bsz,)
            rm_score = rm_score[torch.arange(batch_size), eos_mask_idx]
            return rm_score

    def _expand_to_token_level(self, data: DataProto, scores: torch.Tensor):
        batch_size = data.batch.batch_size[0]
        # expand as token_level_reward
        attention_mask = data.batch["attention_mask"]
        position_ids = data.batch["position_ids"]
        response_length = data.batch["responses"].shape[-1]
        eos_mask_idx = torch.argmax(position_ids * attention_mask, dim=-1)  # (bsz,)
        token_level_scores = torch.zeros_like(attention_mask, dtype=scores.dtype)  # (bsz, seqlen)
        token_level_scores[torch.arange(batch_size), eos_mask_idx] = scores

        # select the response part
        token_level_scores = token_level_scores[:, -response_length:]

        return token_level_scores

    def _switch_chat_template(self, data: DataProto):
        src_max_length = data.batch["attention_mask"].shape[-1]

        src_tokenizer = self.input_tokenizer
        target_tokenizer = self.tokenizer

        rm_input_ids = []
        rm_attention_mask = []

        for i in range(data.batch.batch_size[0]):
            # extract raw prompt
            if isinstance(data.non_tensor_batch["raw_prompt"][i], list):
                chat: list = data.non_tensor_batch["raw_prompt"][i]
            else:
                chat: list = data.non_tensor_batch["raw_prompt"][i].tolist()

            # extract response
            response_ids = data.batch["responses"][i]
            response_length = response_ids.shape[-1]
            valid_response_length = data.batch["attention_mask"][i][-response_length:].sum()
            valid_response_ids = response_ids[:valid_response_length]

            # decode
            response = src_tokenizer.decode(valid_response_ids)
            # remove bos and eos
            response = response.replace(src_tokenizer.eos_token, "")

            chat.append({"role": "assistant", "content": response})

            prompt_with_chat_template = target_tokenizer.apply_chat_template(
                chat, add_generation_prompt=False, tokenize=False
            )
            if self.rank == 0 and i == 0:
                # for debugging purpose
                print(f"Switch template. chat: {prompt_with_chat_template}")

            # the maximum length is actually determined by the reward model itself
            max_length = self.config.get("max_length", src_max_length)
            if max_length is None:
                max_length = src_max_length

            model_inputs = target_tokenizer(prompt_with_chat_template, return_tensors="pt", add_special_tokens=False)
            input_ids, attention_mask = verl_F.postprocess_data(
                input_ids=model_inputs["input_ids"],
                attention_mask=model_inputs["attention_mask"],
                max_length=max_length,
                pad_token_id=target_tokenizer.pad_token_id,
                left_pad=False,  # right padding
                truncation=self.config.get("truncation", "right"),
            )  # truncate from the right

            rm_input_ids.append(input_ids)
            rm_attention_mask.append(attention_mask)

        rm_input_ids = torch.cat(rm_input_ids, dim=0)
        rm_attention_mask = torch.cat(rm_attention_mask, dim=0)

        rm_position_ids = compute_position_id_with_mask(rm_attention_mask)

        rm_inputs = {"input_ids": rm_input_ids, "attention_mask": rm_attention_mask, "position_ids": rm_position_ids}

        return DataProto.from_dict(rm_inputs)

    @register(dispatch_mode=Dispatch.DP_COMPUTE_PROTO)
    def compute_rm_score(self, data: DataProto):
        import itertools

        from verl.utils.seqlen_balancing import get_reverse_idx, rearrange_micro_batches

        # Support all hardwares
        data = data.to(get_device_id())
        if self._do_switch_chat_template:
            rm_data = self._switch_chat_template(data)
        else:
            rm_input_ids = data.batch["input_ids"]
            rm_attention_mask = data.batch["attention_mask"]
            rm_position_ids = data.batch["position_ids"]
            rm_inputs = {
                "input_ids": rm_input_ids,
                "attention_mask": rm_attention_mask,
                "position_ids": rm_position_ids,
            }
            rm_data = DataProto.from_dict(rm_inputs)

        # Support all hardwares
        rm_data.batch = rm_data.batch.to(get_device_id())

        # perform forward computation
        with self.ulysses_sharding_manager:
            rm_data = self.ulysses_sharding_manager.preprocess_data(data=rm_data)
            data = self.ulysses_sharding_manager.preprocess_data(data=data)

            use_dynamic_bsz = self.config.use_dynamic_bsz
            if use_dynamic_bsz:
                max_token_len = self.config.forward_max_token_len_per_gpu * self.ulysses_sequence_parallel_size
                micro_batches, indices = rearrange_micro_batches(batch=rm_data.batch, max_token_len=max_token_len)
            else:
                micro_batches = rm_data.batch.split(self.config.micro_batch_size_per_gpu)
            output = []
            for micro_batch in micro_batches:
                rm_score = self._forward_micro_batch(micro_batch)
                output.append(rm_score)
            scores = torch.cat(output, dim=0)  # (batch_size)

            if use_dynamic_bsz:
                indices = list(itertools.chain.from_iterable(indices))
                assert len(indices) == scores.size(0), f"{len(indices)} vs. {scores.size()}"
                revert_indices = torch.tensor(get_reverse_idx(indices), dtype=torch.long)
                scores = scores[revert_indices]

            token_level_scores = self._expand_to_token_level(data, scores)
            # Note that this is only the scores, may not be the final rewards used to train RL
            output = DataProto.from_dict(tensors={"rm_scores": token_level_scores})
            output = self.ulysses_sharding_manager.postprocess_data(data=output)

        # https://pytorch.org/docs/stable/notes/fsdp.html#fsdp-notes
        # unshard the root FSDP module
        self.reward_module._handle.reshard(True)

        output = output.to("cpu")
        return output
