"""
2026.6.7
2026.6.9
5.5.0
1.7.0
__UNSLOTH_VERSIONING__
"""

# Unsloth auto generated code
# Copyright 2023-present Daniel Han-Chen, Michael Han-Chen & the Unsloth team. All rights reserved.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU Lesser General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.

from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import functional as F
from unsloth_zoo.temporary_patches.common import torch_compile
from typing import Any, List, Optional, Tuple, Union, Dict, Set, Callable
from trl.trainer.grpo_trainer import (Any, AutoModelForSequenceClassification, AutoProcessor, AutoTokenizer, BaseTunerLayer, Callable, CommitScheduler, Dataset, DatasetCard, DatasetCardData, DistributedBackend, EnvironmentFactory, GRPOConfig, GRPOTrainer, GenerationConfig, IterableDataset, LoraConfig, Path, PeftConfig, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, RepeatSampler, RewardFunc, RolloutFunc, Sampler, SyncRefModelCallback, TrainerCallback, VLLMGeneration, Version, _BaseTrainer, _ForwardRedirection, add_response_schema, apply_chat_template, asyncio, atexit, copy, create_model_from_path, create_repo, defaultdict, deque, disable_dropout_in_model, disable_gradient_checkpointing, gather, gather_object, get_config_model_id, get_peft_model, get_training_chat_template, identity, inspect, is_chat_template_prefix_preserving, is_conversational, is_jmespath_available, is_liger_kernel_available, is_peft_available, is_peft_model, is_rich_available, logger, math, nanmax, nanmin, nanstd, nn, np, nullcontext, os, pad, parse_response, pd, peft, pkg_resources, prepare_deepspeed, prepare_fsdp, prepare_multimodal_messages, print_prompt_completions_sample, profiling_context, profiling_decorator, selective_log_softmax, set_seed, shuffle_sequence_dict, shutdown_event_loop_in_daemon, split_pixel_values_by_grid, split_tensor_dict, start_event_loop_in_daemon, supports_tool_calling, sys, textwrap, time, torch, transformers, unsplit_pixel_values_by_grid, unwrap_model_for_generation, use_adapter, warnings, AutoModelForSequenceClassification, AutoProcessor, AutoTokenizer, BaseTunerLayer, Callable, CommitScheduler, Dataset, DatasetCard, DatasetCardData, DistributedBackend, EnvironmentFactory, GRPOConfig, GRPOTrainer, GenerationConfig, IterableDataset, LoraConfig, PeftConfig, PeftModel, PreTrainedModel, PreTrainedTokenizerBase, ProcessorMixin, RewardFunc, RolloutFunc, SyncRefModelCallback, TrainerCallback, VLLMGeneration, Version, add_response_schema, atexit, copy, create_model_from_path, create_repo, defaultdict, deque, disable_dropout_in_model, gather, get_config_model_id, get_peft_model, get_training_chat_template, identity, inspect, is_chat_template_prefix_preserving, is_jmespath_available, is_liger_kernel_available, is_peft_available, is_peft_model, logger, nn, np, os, pad, parse_response, pd, peft, pkg_resources, prepare_deepspeed, prepare_fsdp, set_seed, shutdown_event_loop_in_daemon, start_event_loop_in_daemon, supports_tool_calling, sys, time, torch, transformers, warnings, Version, copy, gather, is_conversational, np, os, pad, parse_response, profiling_context, torch, transformers, Any, apply_chat_template, copy, disable_gradient_checkpointing, gather, gather_object, is_conversational, math, nanmax, nanmin, nanstd, np, os, pad, pd, peft, prepare_multimodal_messages, torch, use_adapter, gather, np, os, pad, profiling_context, torch, transformers, unwrap_model_for_generation, math, np, os, pad, selective_log_softmax, torch, transformers, Any, np, profiling_decorator, shuffle_sequence_dict, split_pixel_values_by_grid, split_tensor_dict, torch, unsplit_pixel_values_by_grid, PeftModel, PreTrainedModel, is_peft_available, logger, os, peft, torch, GRPOTrainer, gather, inspect, nanmax, nanmin, np, os, pad, time, torch)


import os
import math
import logging
from typing import *
from dataclasses import dataclass, field
from packaging.version import Version
import torch
import numpy as np
from contextlib import nullcontext
from torch.nn import functional as F
import inspect
from transformers import DataCollatorForSeq2Seq, DataCollatorForLanguageModeling as TransformersDataCollatorForLanguageModeling
from transformers.training_args import ParallelMode
from unsloth_zoo.device_type import DEVICE_TYPE, device_synchronize

# Wrap trainer with padding to right and enable training mode
import functools
from types import MethodType
try:
    from unsloth_zoo.gradient_checkpointing import reset_unsloth_gradient_checkpointing_buffers
except:
    def reset_unsloth_gradient_checkpointing_buffers(): pass
# Canonical reset lives in unsloth.models._utils so the SFT auto-packing wrapper and the plain
# Trainer loop can import the same helper; fall back to a no-op only if it can't be imported.
try:
    from unsloth.models._utils import _unsloth_reset_stray_compile_cache
except Exception:
    def _unsloth_reset_stray_compile_cache(self): pass
def prepare_for_training_mode(f):
    @functools.wraps(f)
    def wrapper(self, *args, **kwargs):
        # Drop any torch.compile graph cache poisoned by a stray pre-train forward.
        try:
            _unsloth_reset_stray_compile_cache(self)
        except Exception:
            pass
        # Finish the previous W&B run if this is a subsequent train() call.
        # We do this at the START of train() (not the end) so that
        # evaluate() / log() still work after train() completes.
        # HF's WandbCallback.setup() will call wandb.init() for the new run.
        # See: https://github.com/unslothai/unsloth/issues/3954
        if getattr(self, '_unsloth_training_completed', False):
            try:
                import wandb
                if wandb.run is not None:
                    wandb.finish()
                    # Reset HF's WandbCallback so it calls wandb.init() for the new run
                    for cb in self.callback_handler.callbacks:
                        if type(cb).__name__ == 'WandbCallback':
                            cb._initialized = False
                            break
            except:
                pass
        # Enable training mode
        _was_training = None
        # Get gradient checkpointing setting from training arguments
        use_gc = getattr(self.args, 'gradient_checkpointing', True)
        if hasattr(self, 'model') and hasattr(self.model, "training"):
            _was_training = self.model.training
        if hasattr(self, 'model') and hasattr(self.model, "for_training"):
            self.model.for_training(use_gradient_checkpointing=use_gc)
        output = f(self, *args, **kwargs)
        # Restore previous mode when possible
        if hasattr(self, 'model') and hasattr(self.model, "for_inference"):
            if _was_training is False:
                self.model.for_inference()
            elif _was_training is True and hasattr(self.model, "for_training"):
                self.model.for_training(use_gradient_checkpointing=use_gc)
        # Reset gradient checkpointing buffers to free memory while staying ready for next run
        try:
            reset_unsloth_gradient_checkpointing_buffers()
        except:
            pass
        # Mark that training completed so the next train() call can
        # finish this W&B run before starting a new one
        self._unsloth_training_completed = True
        return output
    return wrapper
pass

torch_compile_options = {
            "epilogue_fusion"   : True,
            "max_autotune"      : False,
            "shape_padding"     : True,
            "trace.enabled"     : False,
            "triton.enable_persistent_tma_matmul": torch.cuda.get_device_capability()[0] >= 9,
            "cuda.cutlass_epilogue_fusion_enabled": torch.cuda.get_device_capability()[0] >= 9,
            "cuda.cutlass_tma_only": torch.cuda.get_device_capability()[0] >= 9,
            "cuda.compile_opt_level"              : "-O2",
            "cuda.enable_cuda_lto"                : True,
        }

@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
def chunked_hidden_states_selective_log_softmax(
    hidden_states: torch.Tensor,
    lm_head: torch.Tensor,
    index: torch.Tensor,
    chunks: int = 4,
    logit_scale_multiply: float = 0.0,
    logit_scale_divide: float = 0.0,
    logit_softcapping: float = 0.0,
    temperature: float = 1.0,
) -> torch.Tensor:
    # All Unsloth Zoo code licensed under AGPL3
    flat_hidden_states = hidden_states.reshape(-1, hidden_states.shape[-1])
    flat_index = index.reshape(-1)

    chunked_hidden_states = torch.chunk(flat_hidden_states, chunks=chunks, dim=0)
    chunked_index = torch.chunk(flat_index, chunks=chunks, dim=0)

    all_per_token_logps = []

    for chunk_hidden_states, chunk_index in zip(chunked_hidden_states, chunked_index):
        chunk_logits = chunk_hidden_states.to(lm_head.dtype) @ lm_head.t()

        if logit_scale_multiply != 0.0:
            chunk_logits = chunk_logits * logit_scale_multiply
        if logit_scale_divide != 0.0:
            chunk_logits = chunk_logits / logit_scale_divide
        if logit_softcapping != 0.0:
            chunk_logits = logit_softcapping * torch.tanh(chunk_logits / logit_softcapping)

        chunk_logits = chunk_logits.to(torch.float32)

        if temperature != 1.0:
            chunk_logits = chunk_logits / temperature

        selected_logits = torch.gather(chunk_logits, dim=-1, index=chunk_index.unsqueeze(-1)).squeeze(-1)
        logsumexp_values = torch.logsumexp(chunk_logits, dim=-1)
        per_token_logps = selected_logits - logsumexp_values
        all_per_token_logps.append(per_token_logps)

    all_per_token_logps = torch.concat(all_per_token_logps)

    all_per_token_logps = all_per_token_logps.reshape((hidden_states.shape[0], hidden_states.shape[1]))
    return all_per_token_logps

@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options,)
def chunked_selective_log_softmax(
    logits,
    index,
    temperature: float = 1.0,
    chunks: int = 4,
):
    chunked_logits = torch.chunk(logits.reshape(-1, logits.shape[-1]), chunks = chunks, dim = 0)
    chunked_index  = torch.chunk(index.reshape(-1), chunks = chunks, dim = 0)
    all_per_token_logps = []
    # Per-chunk selective_log_softmax.
    for chunk_logits, chunk_index in zip(chunked_logits, chunked_index):
        chunk_logits = chunk_logits.to(torch.float32)
        if temperature != 1.0:
            chunk_logits = chunk_logits / temperature
        selected_logits = torch.gather(chunk_logits, dim = -1, index = chunk_index.unsqueeze(-1)).squeeze(-1)
        logsumexp_values = torch.logsumexp(chunk_logits, dim = -1)
        per_token_logps = selected_logits - logsumexp_values
        all_per_token_logps.append(per_token_logps)
    pass
    all_per_token_logps = torch.concat(all_per_token_logps)
    all_per_token_logps = all_per_token_logps.reshape((logits.shape[0], logits.shape[1]))
    return all_per_token_logps

def calculate_pad_tokens_in_prompt(
    input_ids: torch.Tensor,
    logits_to_keep: int,
    pad_token_id: int
) -> torch.Tensor:
    """Count left-padded tokens per sequence, e.g. [pad, pad, pad, cat] -> 3."""
    if logits_to_keep >= input_ids.shape[1]:
        raise ValueError("logits_to_keep must be smaller than the sequence length.")

    prompt_section = input_ids[:, :-logits_to_keep]

    padding_mask = (prompt_section == pad_token_id)

    pad_token_counts = padding_mask.sum(dim=1)

    return pad_token_counts

def create_completion_attention_mask(
    completion_input_ids: torch.Tensor,
    left_pad_tokens_per_prompt: torch.Tensor,
    max_left_pad: int,
    pad_token_id: int
) -> torch.Tensor:
    """Build a completion mask that zeros leading prompt and trailing pad tokens.

    For [p,p,p,c,c,c,pad,pad,pad] (p=sliced prompt, c=completion, pad=padding)
    this returns [0,0,0,1,1,1,0,0,0].
    """
    batch_size, completion_len = completion_input_ids.shape
    device = completion_input_ids.device

    num_tokens_to_mask = max_left_pad - left_pad_tokens_per_prompt

    indices = torch.arange(completion_len, device=device).unsqueeze(0)
    shift_mask = indices >= num_tokens_to_mask.unsqueeze(1)

    non_padding_mask = (completion_input_ids != pad_token_id)

    final_mask = shift_mask & non_padding_mask

    return final_mask

def left_pack_padding(tensor: torch.Tensor, pad_id: int) -> torch.Tensor:
    """Move all padding tokens in each sequence to the right."""
    mask = (tensor != pad_id)
    # stable=True since the binary mask is unordered.
    sorted_indices = torch.argsort(mask, dim=1, descending=True, stable=True)
    packed_tensor = torch.gather(tensor, 1, sorted_indices)
    return packed_tensor

def align_logprobs_with_mask(
    logprob_tensor: torch.Tensor,
    attention_mask: torch.Tensor,
    pad_value: float = 0.0
) -> torch.Tensor:
    """Align a log probability tensor with a given attention mask."""

    device = logprob_tensor.device
    batch_size, logprob_seq_len = logprob_tensor.shape
    mask_seq_len = attention_mask.shape[1]

    padded_logprobs = torch.full(
        attention_mask.shape,
        fill_value=pad_value,
        dtype=logprob_tensor.dtype,
        device=device
    )

    left_pad_counts = torch.argmax(attention_mask, dim=1)

    cols = torch.arange(logprob_seq_len, device=device)
    dest_indices = left_pad_counts.unsqueeze(1) + cols

    # Destination row indices, shape [batch_size, logprob_seq_len].
    row_indices = torch.arange(batch_size, device=device).unsqueeze(1).expand_as(dest_indices)

    # Keep only in-bounds destinations, then scatter via advanced indexing.
    valid_mask = dest_indices < mask_seq_len
    valid_rows = row_indices[valid_mask]
    valid_cols = dest_indices[valid_mask]
    valid_vals = logprob_tensor[valid_mask]
    padded_logprobs[valid_rows, valid_cols] = valid_vals

    return padded_logprobs

def align_completion_tool_mask(
    tool_mask: torch.Tensor,
    completion_mask: torch.Tensor,
) -> torch.Tensor:
    """Align a raw completion-length tool/env mask with Unsloth's repacked loss mask."""
    if tool_mask is None:
        return completion_mask
    if tool_mask.shape[0] != completion_mask.shape[0]:
        raise ValueError("tool_mask batch size must match completion_mask batch size.")

    tool_mask = tool_mask.to(device=completion_mask.device)
    if tool_mask.shape == completion_mask.shape:
        aligned_tool_mask = tool_mask
    else:
        aligned_tool_mask = align_logprobs_with_mask(
            tool_mask,
            completion_mask,
            pad_value=0,
        )
    return completion_mask * aligned_tool_mask.to(dtype=completion_mask.dtype)

def autotune_batch_and_chunks(
    total_input_rows,
    seq_len,
    hidden_size,
    vocab_size,
    dtype_bytes=16,
    multiplier=None
):
    if multiplier is None:
        final_m = max(4, seq_len // 4096)
    else:
        final_m = multiplier

    if torch.cuda.is_available():
        free_bytes, _ = torch.cuda.mem_get_info()
        limit_gb = (free_bytes / (1024**3))*.80
    elif hasattr(torch, "xpu") and torch.xpu.is_available():
        # XPU: estimate free memory as total - reserved.
        total_mem = torch.xpu.get_device_properties(0).total_memory
        reserved_mem = torch.xpu.memory_reserved()
        free_bytes = total_mem - reserved_mem
        limit_gb = (free_bytes / (1024**3)) * 0.80
    else:
        # Fallback: assume 8GB available.
        limit_gb = 8.0

    bytes_to_gb = 1024**3

    b_vals = torch.arange(total_input_rows, 0, -1, device='cpu', dtype=torch.float32)

    hidden_gb = (b_vals * seq_len * hidden_size * dtype_bytes) / bytes_to_gb

    base_logits = ((b_vals/total_input_rows) * b_vals * seq_len * vocab_size * dtype_bytes) / bytes_to_gb
    logits_gb = base_logits / final_m

    total_mem_gb = hidden_gb + logits_gb

    valid_mask = total_mem_gb <= limit_gb
    valid_indices = torch.nonzero(valid_mask, as_tuple=False)

    if valid_indices.shape[0] == 0:
        #This means your GPU will OOM
        return 4, final_m

    best_idx = valid_indices[0].item()
    final_b = int(b_vals[best_idx].item())

    return final_b, final_m

def sanitize_logprob(logprob):
    """Local port of trl.scripts.vllm_serve.sanitize_logprob.
    Filters NaN logprobs from vLLM outputs."""
    value = logprob.logprob
    if math.isnan(value):
        logging.getLogger(__name__).warning(
            f"Generated NaN logprob, token logprob '{logprob}' will be ignored"
        )
        return None
    return value
def _unsloth_get_final_logit_softcapping(config):
    """Return final_logit_softcapping for a model config, falling back to the
    nested text sub-config for composite models. Handles both:
      - Gemma-4-style configs where the attribute lives on ``config.text_config``
      - T5Gemma-style composite configs where the text sub-config is only
        reachable via ``config.get_text_config()``
    Returns 0 if unset, matching the previous behaviour.
    """
    softcap = getattr(config, "final_logit_softcapping", None)
    if softcap is None:
        text_cfg = getattr(config, "text_config", None)
        if text_cfg is None:
            get_text_config = getattr(config, "get_text_config", None)
            if callable(get_text_config):
                try:
                    text_cfg = get_text_config()
                except (TypeError, ValueError):
                    text_cfg = None
        if text_cfg is not None and text_cfg is not config:
            softcap = getattr(text_cfg, "final_logit_softcapping", None)
    return 0 if softcap is None else softcap

def _unsloth_get_mm_token_id(processing_class, attr_name, token):
    tokenizer = getattr(processing_class, "tokenizer", processing_class)
    token_id = getattr(processing_class, attr_name, None)
    if token_id is None:
        token_id = getattr(tokenizer, attr_name, None)

    convert_tokens_to_ids = getattr(tokenizer, "convert_tokens_to_ids", None)
    if token_id is None and convert_tokens_to_ids is not None:
        token_id = convert_tokens_to_ids(token)

    if type(token_id) is int and token_id >= 0:
        if token_id != getattr(tokenizer, "unk_token_id", None):
            return token_id
    return None

def _unsloth_fix_mm_token_type_ids(
    processing_class, input_ids, mm_token_type_ids = None, completion_ids = None
):
    image_token_id = _unsloth_get_mm_token_id(
        processing_class, "image_token_id", "<|image_pad|>"
    )
    video_token_id = _unsloth_get_mm_token_id(
        processing_class, "video_token_id", "<|video_pad|>"
    )

    if image_token_id is not None or video_token_id is not None:
        rebuilt = input_ids.new_zeros(input_ids.shape)
        if image_token_id is not None:
            rebuilt = rebuilt.masked_fill(input_ids == image_token_id, 1)
        if video_token_id is not None:
            rebuilt = rebuilt.masked_fill(input_ids == video_token_id, 2)
        return rebuilt

    if (
        mm_token_type_ids is not None
        and completion_ids is not None
        and mm_token_type_ids.shape[0] == input_ids.shape[0]
        and mm_token_type_ids.shape[1] + completion_ids.shape[1] == input_ids.shape[1]
    ):
        return torch.cat(
            [mm_token_type_ids, mm_token_type_ids.new_zeros(completion_ids.shape)],
            dim = 1,
        )
    return mm_token_type_ids

def _unsloth_clear_stateful_mrope(model):
    modules = getattr(model, "modules", None)
    if modules is None:
        return False

    cleared = False
    for module in modules():
        if hasattr(module, "compute_3d_position_ids") and hasattr(module, "rope_deltas"):
            module.rope_deltas = None
            cleared = True
    return cleared

def grpo_compute_loss(
    ref,
    new,
    old,
    sampling_per_token_logps,
    input_ids,
    mask,
    beta,
    advantages,
    **kwargs
):
    # All Unsloth Zoo code licensed under AGPL3
    # Optional argument defaults.
    loss_type = kwargs.get("loss_type", "grpo")
    epsilon_low = kwargs.get("epsilon_low", 0.2)
    epsilon_high = kwargs.get("epsilon_high", 0.2)
    max_completion_length = kwargs.get("max_completion_length", 8192)
    delta = kwargs.get("delta", None)
    importance_sampling_level = kwargs.get("importance_sampling_level", "token")
    num_items_in_batch = kwargs.get("num_items_in_batch", None)
    current_gradient_accumulation_steps = kwargs.get("current_gradient_accumulation_steps", 1)
    num_processes = kwargs.get("num_processes", 1)
    use_vllm = kwargs.get("use_vllm", False)
    vllm_importance_sampling_cap = kwargs.get("vllm_importance_sampling_cap", 2.0)
    get_sapo_token_loss = kwargs.get("get_sapo_token_loss", None)
    sapo_temperature_pos = kwargs.get("sapo_temperature_pos", 1.0)
    sapo_temperature_neg = kwargs.get("sapo_temperature_neg", 1.05)
    get_gamma_weights = kwargs.get("get_gamma_weights", None)
    vespo_k_pos = kwargs.get("vespo_k_pos", 2.0)
    vespo_lambda_pos = kwargs.get("vespo_lambda_pos", 3.0)
    vespo_k_neg = kwargs.get("vespo_k_neg", 3.0)
    vespo_lambda_neg = kwargs.get("vespo_lambda_neg", 2.0)
    get_off_policy_mask = kwargs.get("get_off_policy_mask", None)
    off_policy_mask_threshold  = kwargs.get("off_policy_mask_threshold", None)
    input_ids = input_ids.unsqueeze(-1)

    if advantages.dim() == 1:
        advantages = advantages.unsqueeze(1)

    if off_policy_mask_threshold is not None:
        off_policy_mask = get_off_policy_mask(
            advantages=advantages,
            per_token_logps=new,
            old_per_token_logps=old,
            mask=mask,
            off_policy_threshold=off_policy_mask_threshold,
        )

    with torch.no_grad():
        if use_vllm and sampling_per_token_logps is not None:
            # Filter out extra leading prompt tokens after left-padding input_ids.
            importance_sampling_ratio = torch.exp((old * mask) - sampling_per_token_logps)
            importance_sampling_ratio = torch.clamp(
                importance_sampling_ratio, max=vllm_importance_sampling_cap
            )
    pass

    # Must detach when old is None: exp(new - new.detach()) == 1 but keeps grads correct.
    if old is not None:
        log_ratio = new - old
    else:
        log_ratio = new - new.detach()

    if importance_sampling_level == "token":
        log_importance_weights = log_ratio
    elif importance_sampling_level == "sequence":
        log_importance_weights = (log_ratio * mask).sum(-1) / mask.sum(-1).clamp(min=1.0)
        log_importance_weights = log_importance_weights.unsqueeze(-1)
    else:
        raise ValueError(
            f"Unknown importance sampling level: {importance_sampling_level}. Possible values are 'token' "
            "and 'sequence'."
        )

    coef_1 =  torch.exp(log_importance_weights)

    # Reverse KL: low-variance low-bias estimator as used in the GRPO paper.
    if beta != 0.0:
        kl_i = torch.exp(ref - new) - (ref - new) - 1.0

    else:
        # Zeros with the correct shape.
        if importance_sampling_level == "sequence":
            kl_i = new.new_zeros(new.size(0), 1)
        else:
            kl_i = torch.zeros_like(new)

    if loss_type == "cispo":
        clamped_ratios = torch.clamp(coef_1, max=epsilon_high).detach()
        loss_i = -clamped_ratios * advantages * new
    elif loss_type in ["grpo", "bnpo", "dr_grpo", "dapo"]:
        coef_2 = torch.clamp(coef_1, 1 - epsilon_low, 1 + epsilon_high)

        if delta is not None:
            loss_1 = torch.clamp(coef_1, max=delta) * advantages
        else:
            loss_1 = coef_1 * advantages
        pass
        loss_2 = coef_2 * advantages
        loss_i = -torch.min(loss_1, loss_2)
    elif loss_type == "sapo":
        if get_sapo_token_loss is None:
            raise Exception(f"sapo is only available in TRL 0.26.0+")
        loss_i = torch.empty_like(coef_1)
        positive_advantages_mask = advantages.repeat([1, coef_1.shape[1]]) > 0
        # With n_chunks some tensors may be empty; guard the indexing.
        if coef_1[positive_advantages_mask].numel() != 0:
            loss_i[positive_advantages_mask] = get_sapo_token_loss(
                coef_1[positive_advantages_mask], sapo_temperature_pos
            )
        if coef_1[~positive_advantages_mask].numel() != 0:
            loss_i[~positive_advantages_mask] = get_sapo_token_loss(
                coef_1[~positive_advantages_mask], sapo_temperature_neg
            )
        loss_i = -loss_i * advantages
    elif loss_type == "vespo":
        if get_gamma_weights is None:
            raise Exception("vespo is only available in TRL 0.26.0+")
        phi_seq = get_gamma_weights(
            advantages=advantages,
            log_ratio_per_token=log_ratio,
            mask=mask,
            importance_sampling_ratio=kwargs.get("importance_sampling_ratio"),
            k_pos=vespo_k_pos,
            lambda_pos=vespo_lambda_pos,
            k_neg=vespo_k_neg,
            lambda_neg=vespo_lambda_neg,
        )
        loss_i = -phi_seq * advantages * new
    else:
        raise ValueError(f"Unknown loss type: {loss_type}")

    if off_policy_mask_threshold is not None:
        loss_i = loss_i * off_policy_mask

    if use_vllm and sampling_per_token_logps is not None:
        loss_i = loss_i * importance_sampling_ratio
        # delta for the metric.
        with torch.no_grad():
            delta = torch.abs(old - sampling_per_token_logps)
            delta = delta * mask
            flat_is_ratio = importance_sampling_ratio * mask
    else:
        delta = torch.tensor([]).detach()
        flat_is_ratio = torch.tensor([]).detach()
    if beta != 0.0:
        loss_i = loss_i + beta * kl_i

    mask = mask.to(torch.float32)
    n_mask_per_reward = mask.sum(1)

    # https://github.com/huggingface/trl/blob/e8b8499f1f8d76838155b515e414ee98f757d6d5/trl/trainer/grpo_trainer.py#L1624
    if loss_type in ["grpo", "sapo"]:
        loss = ((loss_i * mask).sum(-1) / mask.sum(-1).clamp(min=1.0)).mean()
        loss = loss / current_gradient_accumulation_steps
    elif loss_type == "bnpo":
        loss = (loss_i * mask).sum() / mask.sum().clamp(min=1.0)
        loss = loss / current_gradient_accumulation_steps
    elif loss_type == "dr_grpo":
        loss = (loss_i * mask).sum() / (loss_i.size(0) * max_completion_length)
        loss = loss / current_gradient_accumulation_steps
    elif loss_type in ["cispo", "dapo", "vespo"]:
        normalizer = num_items_in_batch/ num_processes
        loss = (loss_i * mask).sum() / normalizer
    else:
        raise ValueError(f"Unknown loss type: {loss_type}")

    # Folded metrics.
    def masked_batch_mean(x):
        with torch.inference_mode():
            completion_length = n_mask_per_reward.mean()
            if x.shape[1] == 1:  # when importance_sampling_level == "sequence"
                return completion_length, x.mean()
            else:
                mean_kl_per_reward = (x * mask).sum(1) / n_mask_per_reward
                mean_kl = mean_kl_per_reward.mean()
                return completion_length, mean_kl
    completion_length, mean_kl = masked_batch_mean(kl_i)
    return loss, completion_length, mean_kl, delta, flat_is_ratio, coef_1, mask

class UnslothEfficientGRPO(torch.autograd.Function):
    # All Unsloth Zoo code licensed under AGPL3
    @staticmethod
    def forward(ctx, _new_logps, _old_logps, _ref_logps, _sampling_per_token_logps, lm_head, _input_ids, _mask, _advantages, beta, scaler = None, n_chunks = 1, extra_kwargs=None):
        if extra_kwargs is None:
            extra_kwargs = {}
        def compute_loss(new_logps, old_logps, ref_logps, sampling_per_token_logps, input_ids, mask, advantages, scaling):
            loss, completion_length, mean_kl, delta, flat_is_ratio, coef_1, _mask  = grpo_compute_loss(
                ref_logps,
                new_logps,
                old_logps,
                sampling_per_token_logps,
                input_ids,
                mask,
                beta,
                advantages,
                **extra_kwargs,
            )

            # Scale for mixed precision; return loss.detach() or autograd uses 2x VRAM.
            scaled_loss = loss * scaling
            return scaled_loss, (loss.detach(), completion_length, mean_kl, delta, flat_is_ratio, coef_1)
        pass

        device =_new_logps.device
        grad_inputs = torch.empty_like(_new_logps)
        accumulated_loss              = torch.zeros(1, device = device)[0]
        accumulated_completion_length = torch.zeros(1, device = device)[0]
        accumulated_mean_kl           = torch.zeros(1, device = device)[0]
        accumulated_delta             = []
        accumulated_flat_is_ratio     = []
        accumulated_coef_1            = []

        def accumulate_chunk(
            new_logps_j,
            old_logps_j,
            ref_logps_j,
            sampling_per_token_logps_j,
            input_ids_j,
            mask_j,
            advantages_j,
            scaling,
            grad_inputs_j,
        ):
            (chunk_grad_input,), (chunk_loss, (unscaled_loss, chunk_completion_length, chunk_mean_kl, chunk_delta, chunk_flat_is_ratio, chunk_coef_1)) = torch.func.grad_and_value(
                compute_loss,
                argnums = (0,),
                has_aux = True,
            )(new_logps_j, old_logps_j, ref_logps_j, sampling_per_token_logps_j, input_ids_j, mask_j, advantages_j, scaling)
            accumulated_loss             .add_(unscaled_loss)
            accumulated_completion_length.add_(chunk_completion_length)
            accumulated_mean_kl          .add_(chunk_mean_kl)
            accumulated_delta            .append(chunk_delta)
            accumulated_flat_is_ratio    .append(chunk_flat_is_ratio)
            accumulated_coef_1           .append(chunk_coef_1)
            grad_inputs_j[:] = chunk_grad_input
        pass

        accumulate_chunk = torch.compile(
            accumulate_chunk,
            fullgraph = True,
            # [TODO] Dynamic marking causes torch.compile errors if sequence length is long
            dynamic = True,
            options = torch_compile_options,
        )

        grad_inputs_chunks = torch.chunk(grad_inputs,        chunks = n_chunks, dim = 0)
        new_logps  = torch.chunk(_new_logps, chunks = n_chunks, dim = 0)
        if _old_logps is not None:
            old_logps  = torch.chunk(_old_logps, chunks = n_chunks, dim = 0)
        else:
            old_logps = [None] * n_chunks
        if _ref_logps is not None:
            ref_logps  = torch.chunk(_ref_logps, chunks = n_chunks, dim = 0)
        else:
            ref_logps = [None] * n_chunks
        if _sampling_per_token_logps is not None:
            sampling_per_token_logps  = torch.chunk(_sampling_per_token_logps, chunks = n_chunks, dim = 0)
        else:
            sampling_per_token_logps = [None] * n_chunks
        input_ids          = torch.chunk(_input_ids,         chunks = n_chunks, dim = 0)
        mask               = torch.chunk(_mask,              chunks = n_chunks, dim = 0)
        advantages         = torch.chunk(_advantages,        chunks = n_chunks, dim = 0)

        # Mixed precision scaling if present.
        scaling = scaler.get_scale() if scaler is not None else 1.0

        for (grad_inputs_j, new_logps_j, old_logps_j, ref_logps_j, sampling_per_token_logps_j, input_ids_j, mask_j, advantages_j, ) in \
            zip(grad_inputs_chunks, new_logps, old_logps, ref_logps, sampling_per_token_logps, input_ids, mask, advantages):

            # [TODO] Dynamic marking causes torch.compile errors if sequence length is long

            # mark_dynamic(new_hidden_states_j)
            # mark_dynamic(ref_hidden_states_j)
            # if old_hidden_states_j is not None:
            #     mark_dynamic(old_hidden_states_j)
            # mark_dynamic(input_ids_j)
            # mark_dynamic(mask_j)
            accumulate_chunk(
                new_logps_j,
                old_logps_j,
                ref_logps_j,
                sampling_per_token_logps_j,
                input_ids_j,
                mask_j,
                advantages_j,
                scaling,
                grad_inputs_j,
            )
        pass

        grad_inputs                  .div_(n_chunks)
        accumulated_loss             .div_(n_chunks)
        accumulated_completion_length.div_(n_chunks)
        accumulated_mean_kl          .div_(n_chunks)

        if _sampling_per_token_logps is not None:
            accumulated_delta = torch.cat(accumulated_delta, dim=0)
            accumulated_flat_is_ratio = torch.cat(accumulated_flat_is_ratio, dim=0)
        else:
            accumulated_delta = None
            accumulated_flat_is_ratio = None
        accumulated_coef_1  = torch.cat(accumulated_coef_1, dim=0)
        ctx.save_for_backward(grad_inputs)
        return (
            accumulated_loss,
            accumulated_completion_length,
            accumulated_mean_kl,
            accumulated_delta,
            accumulated_flat_is_ratio,
            accumulated_coef_1
        )
    pass

    @staticmethod
    def backward(ctx, grad_output, dcompletion_length, dmean_kl, ddelta, ddflat_is_ratio, dcoef_1):
        (grad_input,) = ctx.saved_tensors
        return (grad_input, None, None, None, None, None, None, None, None, None, None, None)
    pass

def grpo_accumulated_loss(
    trainer,
    input_ids,
    attention_mask,
    logits_to_keep,
    completion_mask,
    advantages,
    old_logps,
    ref_logps,
    n_chunks = -1,
    tool_mask = None,
    **kwargs,
):
    # All Unsloth Zoo code licensed under AGPL3
    bsz, qlen = input_ids.shape

    pixel_values = kwargs.get('pixel_values',None)
    image_grid_thw = kwargs.get('image_grid_thw',None)
    pixel_attention_mask = kwargs.get('pixel_attention_mask',None)
    image_sizes = kwargs.get('image_sizes',None)
    num_images = kwargs.get('num_images',None)
    # Transformers 5.x requires token_type_ids/mm_token_type_ids for some vision models
    token_type_ids = kwargs.get('token_type_ids',None)
    mm_token_type_ids = kwargs.get('mm_token_type_ids',None)
    if mm_token_type_ids is not None or image_grid_thw is not None:
        mm_token_type_ids = _unsloth_fix_mm_token_type_ids(
            trainer.processing_class, input_ids, mm_token_type_ids
        )
    sampling_per_token_logps = kwargs.get("sampling_per_token_logps", None) if getattr(trainer, "vllm_importance_sampling_correction", False) else None
    temperature = kwargs.get("temperature", 1.0)
    logit_scale_multiply = kwargs.get("logit_scale_multiply", 0.0)
    logit_scale_divide   = kwargs.get("logit_scale_divide", 0.0)
    logit_softcapping    = kwargs.get("logit_softcapping", 0.0)
    prev_max_left_pad    = kwargs.get("max_left_pad", 0) # max_left_pad for LLM training, enabled by default.

    # Pop from kwargs to avoid downstream issues.
    _ = kwargs.pop("sampling_per_token_logps", None)
    kwargs["vllm_importance_sampling_cap"] = trainer.vllm_importance_sampling_cap if sampling_per_token_logps is not None else None
    kwargs["get_sapo_token_loss"] = trainer.get_sapo_token_loss if hasattr(trainer, "get_sapo_token_loss") else None
    kwargs["sapo_temperature_pos"] = trainer.args.sapo_temperature_pos if hasattr(trainer.args, "sapo_temperature_pos") else None
    kwargs["sapo_temperature_neg"] = trainer.args.sapo_temperature_neg if hasattr(trainer.args, "sapo_temperature_neg") else None
    kwargs["get_gamma_weights"] = trainer.get_gamma_weights if hasattr(trainer, "get_gamma_weights") else None
    kwargs["vespo_k_pos"] = trainer.args.vespo_k_pos if hasattr(trainer.args, "vespo_k_pos") else 2.0
    kwargs["vespo_k_neg"] = trainer.args.vespo_k_neg if hasattr(trainer.args, "vespo_k_neg") else 3.0
    kwargs["vespo_lambda_pos"] = trainer.args.vespo_lambda_pos if hasattr(trainer.args, "vespo_lambda_pos") else 3.0
    kwargs["vespo_lambda_neg"] = trainer.args.vespo_lambda_neg if hasattr(trainer.args, "vespo_lambda_neg") else 2.0
    kwargs["get_off_policy_mask"] = trainer.get_off_policy_mask if hasattr(trainer, "get_off_policy_mask") else None
    kwargs["off_policy_mask_threshold"] = trainer.args.off_policy_mask_threshold  if hasattr(trainer.args, "off_policy_mask_threshold") else None
    kwargs["use_vllm"] = trainer.use_vllm
    # Snap n_chunks to the closest divisor of bsz.
    factors = [i for i in range(1, bsz + 1) if bsz % i == 0]
    if n_chunks == -1: n_chunks = bsz
    n_chunks = factors[min(np.searchsorted(factors, n_chunks), len(factors)-1)]

    if not hasattr(trainer, '_autocast_dtype'):
        trainer._autocast_dtype = torch.float16 if os.environ.get('ACCELERATE_MIXED_PRECISION', 'fp16') == 'fp16' else torch.bfloat16
        if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1': trainer._autocast_dtype = None
    pass
    os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1"

    lm_head = trainer.model.get_output_embeddings().weight
    dtype_bytes = 16 if trainer._autocast_dtype in [torch.float16, torch.bfloat16] else 32

    total_rows = input_ids.shape[0]
    seq_len = input_ids.shape[1]
    hidden_dim = lm_head.shape[1]
    vocab_dim = lm_head.shape[0]

    if trainer.args.unsloth_grpo_mini_batch is None:
        if not hasattr(trainer, "_has_autotuned"):
            trainer._has_autotuned = True
            B, multiplier = autotune_batch_and_chunks(
                total_rows, seq_len, hidden_dim, vocab_dim, dtype_bytes, trainer.args.unsloth_logit_chunk_multiplier
            )
            trainer.args.unsloth_grpo_mini_batch = max(1, total_rows//B)
            trainer.args.unsloth_logit_chunk_multiplier = multiplier
            B = trainer.args.unsloth_grpo_mini_batch
            multiplier = trainer.args.unsloth_logit_chunk_multiplier
        elif trainer._step % trainer.current_gradient_accumulation_steps == 0:
            B = trainer.args.unsloth_grpo_mini_batch
            multiplier = trainer.args.unsloth_logit_chunk_multiplier
            del trainer._has_autotuned
            del trainer.args.unsloth_grpo_mini_batch
            del trainer.args.unsloth_logit_chunk_multiplier
        else:
            B = trainer.unsloth_grpo_mini_batch
            multiplier = trainer.args.unsloth_logit_chunk_multiplier
    else:
        if trainer.args.unsloth_grpo_mini_batch > total_rows:
            B = total_rows
        else:
            B = trainer.args.unsloth_grpo_mini_batch

        if trainer.args.unsloth_logit_chunk_multiplier is None:
            multiplier = max(4, seq_len // 4096)
        else:
            multiplier = trainer.args.unsloth_logit_chunk_multiplier

    if pixel_values is None:
        left_pad_tokens_per_prompt = calculate_pad_tokens_in_prompt(input_ids, logits_to_keep, trainer.processing_class.pad_token_id)

        # Determine max_left_pad from precomputed logprobs shape for consistency
        if old_logps is not None:
            max_left_pad = old_logps.shape[1] - logits_to_keep
        elif ref_logps is not None:
            max_left_pad = ref_logps.shape[1] - logits_to_keep
        else:
            max_left_pad = torch.max(left_pad_tokens_per_prompt).item()

        input_ids = left_pack_padding(input_ids, trainer.processing_class.pad_token_id)

        completion_input_ids = input_ids[:, -(logits_to_keep +max_left_pad):]
        completion_mask = create_completion_attention_mask(completion_input_ids, left_pad_tokens_per_prompt, max_left_pad, trainer.processing_class.pad_token_id).to(attention_mask.dtype)

        if trainer.use_vllm and sampling_per_token_logps is not None and getattr(trainer, "vllm_importance_sampling_correction", False):
            sampling_per_token_logps = align_logprobs_with_mask(sampling_per_token_logps, completion_mask)
        else:
            sampling_per_token_logps = None
        completion_mask = align_completion_tool_mask(tool_mask, completion_mask)
        attention_mask =  input_ids != trainer.processing_class.pad_token_id
        attention_mask = attention_mask.to(attention_mask.dtype)
    else:
        completion_input_ids = input_ids[:, -logits_to_keep:]
        completion_mask = align_completion_tool_mask(tool_mask, completion_mask)

    unwrapped_model = trainer.accelerator.unwrap_model(trainer.model, keep_fp32_wrapper = False)

    for module in unwrapped_model.modules():
        if hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "io_same_decice"):
            module._hf_hook.io_same_decice = False
    pass

    all_logprobs_list = []

    def slice_sample_axis(value, start, end):
        if value is None:
            return None
        return value[start:end]

    import math
    total_samples = input_ids.shape[0]
    batch_size = math.ceil(total_samples / B)
    if isinstance(num_images, torch.Tensor):
        num_images = num_images.detach().cpu().reshape(-1).tolist()
    if image_grid_thw is not None and pixel_values is not None and num_images is not None:
        rows_per_image = image_grid_thw.prod(dim=-1)
        rows_per_sample = torch.split(rows_per_image, num_images)
        rows_per_sample = torch.stack([s.sum() for s in rows_per_sample])
        cum_rows = torch.cat(
            [
                torch.tensor([0], device=rows_per_sample.device),
                rows_per_sample.cumsum(0),
            ]
        )
        cum_imgs = torch.tensor([0] + num_images).cumsum(0)
    else:
        cum_rows = None
        cum_imgs = None

    input_ids_chunks = []
    attention_mask_chunks = []
    completion_ids_chunks = []
    pixel_values_chunks = []
    image_grid_thw_chunks = []
    pixel_attention_mask_chunks = []
    image_sizes_chunks = []
    token_type_ids_chunks = []
    mm_token_type_ids_chunks = []

    current_pixel_idx = 0
    #TRL 0.23.0 batching logic
    for start in range(0, total_samples, batch_size):
        end = min(start + batch_size, total_samples)

        input_ids_chunks.append(input_ids[start:end])
        attention_mask_chunks.append(attention_mask[start:end])
        completion_ids_chunks.append(completion_input_ids[start:end])
        image_sizes_chunks.append(slice_sample_axis(image_sizes, start, end))
        token_type_ids_chunks.append(slice_sample_axis(token_type_ids, start, end))
        mm_token_type_ids_chunks.append(
            slice_sample_axis(mm_token_type_ids, start, end)
        )

        if image_grid_thw is not None and pixel_values is not None:

            if num_images is None:
                grid_slice = image_grid_thw[start:end]
                batch_pixel_count = grid_slice.prod(dim=-1).sum().item()
                start_pixel_idx = current_pixel_idx
                end_pixel_idx = current_pixel_idx + batch_pixel_count
                current_pixel_idx = end_pixel_idx
            else:
                start_pixel_idx = cum_rows[start].item()
                end_pixel_idx = cum_rows[end].item()
                img_start, img_end = cum_imgs[start], cum_imgs[end]
                grid_slice = image_grid_thw[img_start:img_end]
            image_grid_thw_chunks.append(grid_slice)

            pixel_values_chunks.append(pixel_values[start_pixel_idx:end_pixel_idx])

            if pixel_attention_mask is not None:
                if pixel_attention_mask.shape[0] == pixel_values.shape[0]:
                    pixel_attention_mask_chunks.append(pixel_attention_mask[start_pixel_idx:end_pixel_idx])
                else:
                    pixel_attention_mask_chunks.append(pixel_attention_mask[start:end])
            else:
                pixel_attention_mask_chunks.append(None)

        else:
            pixel_values_chunks.append(None)
            image_grid_thw_chunks.append(None)
            pixel_attention_mask_chunks.append(None)

    zipped_inputs = zip(
        input_ids_chunks,
        attention_mask_chunks,
        pixel_values_chunks,
        image_grid_thw_chunks,
        pixel_attention_mask_chunks,
        image_sizes_chunks,
        token_type_ids_chunks,
        mm_token_type_ids_chunks,
        completion_ids_chunks
    )

    if trainer._autocast_dtype is None:
        autocaster = nullcontext()
    else:
        autocaster = torch.amp.autocast(device_type = trainer.model.device.type, dtype = trainer._autocast_dtype)

    def to_device(tensor, device, non_blocking=True):
        if tensor is None: return None
        return tensor.to(device, non_blocking=non_blocking)

    class Unsloth_Offloaded_Log_Softmax(torch.autograd.Function):
        """Manual gradient checkpointing / CPU offloading for log softmax."""
        @staticmethod
        def forward(ctx, hidden_states, lm_head, index, chunks,
                    logit_scale_multiply, logit_scale_divide,
                    logit_softcapping, temperature):
            # Detach so we don't keep the graph (and extra memory) on CPU.
            ctx.saved_hidden_states = hidden_states.detach().contiguous().to("cpu", non_blocking=True)
            ctx.device = hidden_states.device
            ctx.dtype = hidden_states.dtype

            ctx.lm_head = lm_head
            ctx.lm_head_requires_grad = lm_head.requires_grad
            ctx.index = index
            ctx.args = (chunks, logit_scale_multiply, logit_scale_divide, logit_softcapping, temperature)

            with torch.no_grad():
                output = chunked_hidden_states_selective_log_softmax(
                    hidden_states, lm_head, index, *ctx.args
                )

            return output

        @staticmethod
        def backward(ctx, grad_output):
            hidden_states = to_device(ctx.saved_hidden_states, ctx.device)
            hidden_states = hidden_states.to(ctx.dtype)
            hidden_states.requires_grad_(True)

            lm_head = ctx.lm_head
            # #Possibly redundant lines
            # if ctx.lm_head_requires_grad:
            #     hidden_states.requires_grad_(True)
            # else:
            #     lm_head = lm_head.detach()

            index = ctx.index

            with torch.enable_grad():
                output = chunked_hidden_states_selective_log_softmax(
                    hidden_states, lm_head, index, *ctx.args
                )

            torch.autograd.backward(output, grad_output)

            return (
                hidden_states.grad,
                lm_head.grad if ctx.lm_head_requires_grad else None,
                None,
                None,
                None,
                None,
                None,
                None,
            )

    def efficient_log_softmax(hidden_states, lm_head, index, chunks=32,
                            logit_scale_multiply=0.0, logit_scale_divide=0.0,
                            logit_softcapping=0.0, temperature=1, batch_size=8):
        if (index.shape[1] <= 1024 and batch_size <= 8) or batch_size==1:
            # Normal path is faster / saves a GB under these conditions.
            return chunked_hidden_states_selective_log_softmax(
                hidden_states,
                lm_head,
                index,
                chunks,
                logit_scale_multiply,
                logit_scale_divide,
                logit_softcapping,
                temperature
            )
        else:
            return Unsloth_Offloaded_Log_Softmax.apply(
                hidden_states, lm_head, index, chunks,
                logit_scale_multiply, logit_scale_divide,
                logit_softcapping, temperature
            )

    def compute_logprobs_chunk(new_hidden_states_chunk, completion_ids, input_ids_chunk):
        # Hidden states -> lm_head matmul path; raw logits -> skip matmul and
        # skip scale/softcap (model forward already applied them).
        chunks = input_ids_chunk.shape[0] * multiplier
        if new_hidden_states_chunk.shape[-1] == lm_head.shape[1]:
            return efficient_log_softmax(
                new_hidden_states_chunk,
                lm_head,
                completion_ids,
                chunks = chunks,
                logit_scale_multiply = logit_scale_multiply,
                logit_scale_divide = logit_scale_divide,
                logit_softcapping = logit_softcapping,
                temperature = temperature,
                batch_size = B,
            )
        return chunked_selective_log_softmax(
            new_hidden_states_chunk,
            completion_ids,
            temperature = temperature,
            chunks = chunks,
        )
    for (
        input_ids_chunk,
        attention_mask_chunk,
        pixel_values_chunk,
        image_grid_thw_chunk,
        pixel_attention_mask_chunk,
        image_sizes_chunk,
        token_type_ids_chunk,
        mm_token_type_ids_chunk,
        completion_ids
    ) in zipped_inputs:
            _extra_vision_kwargs = {}
            if token_type_ids_chunk is not None:
                _extra_vision_kwargs["token_type_ids"] = token_type_ids_chunk
            if mm_token_type_ids_chunk is not None:
                _extra_vision_kwargs["mm_token_type_ids"] = mm_token_type_ids_chunk
            with autocaster:
                if pixel_values is None:
                    new_hidden_states_chunk = unwrapped_model(
                        input_ids = input_ids_chunk,
                        attention_mask = attention_mask_chunk,
                        pixel_values = pixel_values_chunk,
                        image_grid_thw = image_grid_thw_chunk,
                        pixel_attention_mask = pixel_attention_mask_chunk,
                        image_sizes = image_sizes_chunk,
                        **_extra_vision_kwargs,
                    ).logits

                    new_hidden_states_chunk = new_hidden_states_chunk[:, -(logits_to_keep + max_left_pad + 1): , :]
                    new_hidden_states_chunk = new_hidden_states_chunk[:, :-1, :]
                    logprobs_chunk = compute_logprobs_chunk(new_hidden_states_chunk, completion_ids, input_ids_chunk)
                else:
                    new_hidden_states_chunk = unwrapped_model(
                        input_ids = input_ids_chunk,
                        attention_mask = attention_mask_chunk,
                        pixel_values = pixel_values_chunk,
                        image_grid_thw = image_grid_thw_chunk,
                        pixel_attention_mask = pixel_attention_mask_chunk,
                        image_sizes = image_sizes_chunk,
                        logits_to_keep = logits_to_keep + 1,
                        **_extra_vision_kwargs,
                    ).logits

                    new_hidden_states_chunk = new_hidden_states_chunk[:, :-1, :]
                    logprobs_chunk = compute_logprobs_chunk(new_hidden_states_chunk, completion_ids, input_ids_chunk)
                # Avoids race conditions with GPT OSS offload_embbed=True; no measurable slowdown.
                device_synchronize()
            all_logprobs_list.append(logprobs_chunk)

    new_logprobs = torch.cat(all_logprobs_list, dim=0)

    with autocaster:
        loss, completion_length, mean_kl, delta, flat_is_ratio, coef_1 = UnslothEfficientGRPO.apply(
            new_logprobs,
            old_logps,
            ref_logps,
            sampling_per_token_logps,
            lm_head,
            completion_input_ids,
            completion_mask,
            advantages,
            trainer.beta,
            trainer.accelerator.scaler,
            1,
            kwargs
        )

    # Force logits (not hidden states) again or output is gibberish.
    os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "0"

    return loss, completion_length, mean_kl, delta, flat_is_ratio, coef_1, completion_mask
    # Old non-efficient code path (dead).
    new_logits = torch.matmul(new_hidden_states, lm_head.t())
    new_logits = new_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred
    old_logits = torch.matmul(old_hidden_states, lm_head.t())
    old_logits = old_logits[:, :-1, :] # exclude the last logit: it corresponds to the next token pred
    loss, completion_length, mean_kl = grpo_compute_loss(
        old_logits,
        new_logits,
        completion_input_ids,
        completion_mask,
        trainer.beta,
        advantages,
    )
    return loss, completion_length, mean_kl
    pass

@torch.compile(dynamic = True, fullgraph = True, options = torch_compile_options)
def grpo_compute_loss_slow(
    ref,
    new,
    old,
    sampling_per_token_logps,
    input_ids,
    mask,
    beta,
    advantages,
    **kwargs
):
    # All Unsloth Zoo code licensed under AGPL3
    # Optional argument defaults.
    loss_type = kwargs.get("loss_type", "grpo")
    epsilon_low = kwargs.get("epsilon_low", 0.2)
    epsilon_high = kwargs.get("epsilon_high", 0.2)
    max_completion_length = kwargs.get("max_completion_length", 8192)
    delta = kwargs.get("delta", None)
    importance_sampling_level = kwargs.get("importance_sampling_level", "token")
    num_items_in_batch = kwargs.get("num_items_in_batch", None)
    current_gradient_accumulation_steps = kwargs.get("current_gradient_accumulation_steps", 1)
    num_processes = kwargs.get("num_processes", 1)
    use_vllm = kwargs.get("use_vllm", False)
    vllm_importance_sampling_cap = kwargs.get("vllm_importance_sampling_cap", 2.0)
    get_sapo_token_loss = kwargs.get("get_sapo_token_loss", None)
    sapo_temperature_pos = kwargs.get("sapo_temperature_pos", 1.0)
    sapo_temperature_neg = kwargs.get("sapo_temperature_neg", 1.05)
    get_gamma_weights = kwargs.get("get_gamma_weights", None)
    vespo_k_pos = kwargs.get("vespo_k_pos", 2.0)
    vespo_lambda_pos = kwargs.get("vespo_lambda_pos", 3.0)
    vespo_k_neg = kwargs.get("vespo_k_neg", 3.0)
    vespo_lambda_neg = kwargs.get("vespo_lambda_neg", 2.0)
    get_off_policy_mask = kwargs.get("get_off_policy_mask", None)
    off_policy_mask_threshold  = kwargs.get("off_policy_mask_threshold", None)
    input_ids = input_ids.unsqueeze(-1)

    if advantages.dim() == 1:
        advantages = advantages.unsqueeze(1)

    if off_policy_mask_threshold is not None:
        off_policy_mask = get_off_policy_mask(
            advantages=advantages,
            per_token_logps=new,
            old_per_token_logps=old,
            mask=mask,
            off_policy_threshold=off_policy_mask_threshold,
        )

    with torch.no_grad():
        if use_vllm and sampling_per_token_logps is not None:
            # Filter out extra leading prompt tokens after left-padding input_ids.
            importance_sampling_ratio = torch.exp((old * mask) - sampling_per_token_logps)
            importance_sampling_ratio = torch.clamp(
                importance_sampling_ratio, max=vllm_importance_sampling_cap
            )
    pass

    # Must detach when old is None: exp(new - new.detach()) == 1 but keeps grads correct.
    if old is not None:
        log_ratio = new - old
    else:
        log_ratio = new - new.detach()

    if importance_sampling_level == "token":
        log_importance_weights = log_ratio
    elif importance_sampling_level == "sequence":
        log_importance_weights = (log_ratio * mask).sum(-1) / mask.sum(-1).clamp(min=1.0)
        log_importance_weights = log_importance_weights.unsqueeze(-1)
    else:
        raise ValueError(
            f"Unknown importance sampling level: {importance_sampling_level}. Possible values are 'token' "
            "and 'sequence'."
        )

    coef_1 =  torch.exp(log_importance_weights)

    # Reverse KL: low-variance low-bias estimator as used in the GRPO paper.
    if beta != 0.0:
        kl_i = torch.exp(ref - new) - (ref - new) - 1.0

    else:
        # Zeros with the correct shape.
        if importance_sampling_level == "sequence":
            kl_i = new.new_zeros(new.size(0), 1)
        else:
            kl_i = torch.zeros_like(new)

    if loss_type == "cispo":
        clamped_ratios = torch.clamp(coef_1, max=epsilon_high).detach()
        loss_i = -clamped_ratios * advantages * new
    elif loss_type in ["grpo", "bnpo", "dr_grpo", "dapo"]:
        coef_2 = torch.clamp(coef_1, 1 - epsilon_low, 1 + epsilon_high)

        if delta is not None:
            loss_1 = torch.clamp(coef_1, max=delta) * advantages
        else:
            loss_1 = coef_1 * advantages
        pass
        loss_2 = coef_2 * advantages
        loss_i = -torch.min(loss_1, loss_2)
    elif loss_type == "sapo":
        if get_sapo_token_loss is None:
            raise Exception(f"sapo is only available in TRL 0.26.0+")
        loss_i = torch.empty_like(coef_1)
        positive_advantages_mask = advantages.repeat([1, coef_1.shape[1]]) > 0
        # With n_chunks some tensors may be empty; guard the indexing.
        if coef_1[positive_advantages_mask].numel() != 0:
            loss_i[positive_advantages_mask] = get_sapo_token_loss(
                coef_1[positive_advantages_mask], sapo_temperature_pos
            )
        if coef_1[~positive_advantages_mask].numel() != 0:
            loss_i[~positive_advantages_mask] = get_sapo_token_loss(
                coef_1[~positive_advantages_mask], sapo_temperature_neg
            )
        loss_i = -loss_i * advantages
    elif loss_type == "vespo":
        if get_gamma_weights is None:
            raise Exception("vespo is only available in TRL 0.26.0+")
        phi_seq = get_gamma_weights(
            advantages=advantages,
            log_ratio_per_token=log_ratio,
            mask=mask,
            importance_sampling_ratio=kwargs.get("importance_sampling_ratio"),
            k_pos=vespo_k_pos,
            lambda_pos=vespo_lambda_pos,
            k_neg=vespo_k_neg,
            lambda_neg=vespo_lambda_neg,
        )
        loss_i = -phi_seq * advantages * new
    else:
        raise ValueError(f"Unknown loss type: {loss_type}")

    if off_policy_mask_threshold is not None:
        loss_i = loss_i * off_policy_mask

    if use_vllm and sampling_per_token_logps is not None:
        loss_i = loss_i * importance_sampling_ratio
        # delta for the metric.
        with torch.no_grad():
            delta = torch.abs(old - sampling_per_token_logps)
            delta = delta * mask
            flat_is_ratio = importance_sampling_ratio * mask
    else:
        delta = torch.tensor([]).detach()
        flat_is_ratio = torch.tensor([]).detach()
    if beta != 0.0:
        loss_i = loss_i + beta * kl_i

    mask = mask.to(torch.float32)
    n_mask_per_reward = mask.sum(1)

    # https://github.com/huggingface/trl/blob/e8b8499f1f8d76838155b515e414ee98f757d6d5/trl/trainer/grpo_trainer.py#L1624
    if loss_type in ["grpo", "sapo"]:
        loss = ((loss_i * mask).sum(-1) / mask.sum(-1).clamp(min=1.0)).mean()
        loss = loss / current_gradient_accumulation_steps
    elif loss_type == "bnpo":
        loss = (loss_i * mask).sum() / mask.sum().clamp(min=1.0)
        loss = loss / current_gradient_accumulation_steps
    elif loss_type == "dr_grpo":
        loss = (loss_i * mask).sum() / (loss_i.size(0) * max_completion_length)
        loss = loss / current_gradient_accumulation_steps
    elif loss_type in ["cispo", "dapo", "vespo"]:
        normalizer = num_items_in_batch/ num_processes
        loss = (loss_i * mask).sum() / normalizer
    else:
        raise ValueError(f"Unknown loss type: {loss_type}")

    # Folded metrics.
    def masked_batch_mean(x):
        with torch.inference_mode():
            completion_length = n_mask_per_reward.mean()
            if x.shape[1] == 1:  # when importance_sampling_level == "sequence"
                return completion_length, x.mean()
            else:
                mean_kl_per_reward = (x * mask).sum(1) / n_mask_per_reward
                mean_kl = mean_kl_per_reward.mean()
                return completion_length, mean_kl
    completion_length, mean_kl = masked_batch_mean(kl_i)
    return loss, completion_length, mean_kl, delta, flat_is_ratio, coef_1, mask

def grpo_update_SamplingParams(SamplingParams, generation_kwargs, vllm_sampling_params = None):
    good_sampling_params_keys = inspect.signature(SamplingParams).parameters.keys()

    new_generation_kwargs = {}
    for key in generation_kwargs.keys():
        if key in good_sampling_params_keys:
            new_generation_kwargs[key] = generation_kwargs[key]
    generation_kwargs = new_generation_kwargs

    if vllm_sampling_params is not None:
        for key in good_sampling_params_keys:
            if hasattr(vllm_sampling_params, key):
                overwrited_key = getattr(vllm_sampling_params, key)
                if overwrited_key is not None and (type(overwrited_key) in (list, tuple,) and len(overwrited_key) != 0):
                    generation_kwargs[key] = overwrited_key
    return generation_kwargs

def _get_inference_mode_context_manager(model: torch.nn.Module):
    """
    If the state dict was quantized using torchao, we will run into
    the following error when calling ops like aten.t() in inference mode.
    This is a bug in PyTorch that affects all tensor subclasses.

        Cannot set version_counter for inference tensor

    For now, we work around this issue by using `torch.no_grad()` in this case.
    See https://github.com/pytorch/pytorch/issues/164872 for more details.
    Otherwise, just return `torch.inference_mode()`.
    """
    torchao_config = getattr(model, "torchao_config", None)
    if torchao_config is not None and torchao_config.qat_scheme is None:
        return torch.no_grad()
    else:
        return torch.inference_mode()
@dataclass
class UnslothGRPOConfig(GRPOConfig):
    """
    
    Configuration class for the [`GRPOTrainer`].

    This class includes only the parameters that are specific to GRPO training. For a full list of training arguments,
    please refer to the [`~transformers.TrainingArguments`] documentation. Note that default values in this class may
    differ from those in [`~transformers.TrainingArguments`].

    Using [`~transformers.HfArgumentParser`] we can turn this class into
    [argparse](https://docs.python.org/3/library/argparse#module-argparse) arguments that can be specified on the
    command line.

    Parameters:
        > Parameters that control the model and reference model

        model_init_kwargs (`str`, `dict[str, Any]`, *optional*):
            Keyword arguments for [`~transformers.AutoModelForCausalLM.from_pretrained`], used when the `model`
            argument of the [`GRPOTrainer`] is provided as a string.
        trust_remote_code (`bool`, *optional*, defaults to `False`):
            Whether to allow loading models and tokenizers that ship custom Python code from the Hub. Forwarded to
            [`~transformers.AutoModelForCausalLM.from_pretrained`] and
            [`~transformers.AutoProcessor.from_pretrained`]. Also applied to reward-model and reward-tokenizer loads.
        router_aux_loss_coef (`float`, *optional*, defaults to `0.001`):
            Coefficient of the load-balancing auxiliary loss. Only has an effect when training a Mixture-of-Experts
            (MoE) model; for other models it does nothing. The auxiliary loss is added to the training loss with this
            weight. Set to `0.0` to disable it.
        disable_dropout (`bool`, *optional*, defaults to `False`):
            Whether to disable dropout in the model. This is useful for training with a reference model, as it prevents
            the model from generating different logprobs for the same input.
        cast_lm_head_to_fp32 (`bool`, *optional*, defaults to `False`):
            Whether to cast the language modeling head of the policy and reference models to float32. As recommended by
            the [ScaleRL](https://huggingface.co/papers/2510.13786) recipe. This flag is only supported when the model
            has untied word embedding and language modeling head layers i.e. `tie_word_embeddings` in the model config
            is False.

        > Parameters that control the data preprocessing

        remove_unused_columns (`bool`, *optional*, defaults to `False`):
            Whether to only keep the column `"prompt"` in the dataset. If you use a custom reward function that
            requires any column other than `"prompts"` and `"completions"`, you should keep this to `False`.
        num_generations (`int`, *optional*, defaults to `8`):
            Number of generations per prompt to sample. The effective batch size (num_processes * per_device_batch_size
            * gradient_accumulation_steps) must be evenly divisible by this value.
        num_generations_eval (`int` or `None`, *optional*):
            Number of generations to sample during evaluation. This allows using fewer generations during evaluation to
            save computation. If `None`, uses the value of `num_generations`.
        max_completion_length (`int` or `None`, *optional*, defaults to `256`):
            Maximum length of the generated completion.
        ds3_gather_for_generation (`bool`, *optional*, defaults to `True`):
            This setting applies to DeepSpeed ZeRO-3. If enabled, the policy model weights are gathered for generation,
            improving generation speed. However, disabling this option allows training models that exceed the VRAM
            capacity of a single GPU, albeit at the cost of slower generation. Disabling this option is not compatible
            with vLLM generation.
        shuffle_dataset (`bool`, *optional*, defaults to `True`):
            Whether to shuffle the training dataset.
        pad_to_multiple_of (`int`, *optional*):
            If set, the prompts ids and completions ids will be padded to a multiple of this value.

        > Parameters that control generation

        generation_batch_size (`int`, *optional*):
            Batch size to use for generation. If `None`, it defaults to the effective training batch size:
            `per_device_train_batch_size * num_processes * steps_per_generation`. In other words, there is one
            generation batch processed per optimization step. Mutually exclusive with `steps_per_generation`.
        steps_per_generation (`int`, *optional*):
            Number of steps per generation. If `None`, it defaults to `gradient_accumulation_steps`. Mutually exclusive
            with `generation_batch_size`.
        temperature (`float`, defaults to `1.0`):
            Temperature for sampling. The higher the temperature, the more random the completions.
        top_p (`float`, *optional*, defaults to `1.0`):
            Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to
            `1.0` to consider all tokens.
        top_k (`int`, *optional*, defaults to `0`):
            Number of highest probability vocabulary tokens to keep for top-k-filtering. If `0`, top-k-filtering is
            disabled and all tokens are considered.
        min_p (`float`, *optional*):
            Minimum token probability, which will be scaled by the probability of the most likely token. It must be a
            value between `0.0` and `1.0`. Typical values are in the `0.01-0.2` range.
        generation_kwargs (`dict[str, Any]`, *optional*):
            Additional keyword arguments to pass to [`~transformers.GenerationConfig`] (if using transformers) or
            `SamplingParams` (if using vLLM) when sampling completions. This can be used to further customize the
            generation behavior, such as setting `suppress_tokens`, `num_beams`, etc. If it contains keys that conflict
            with the other generation parameters (like `min_p`, `top_p`, etc.), they will override them.
        chat_template_kwargs (`dict[str, Any]`, *optional*):
            Additional keyword arguments to pass to the `apply_chat_template` function when generating completions.
        repetition_penalty (`float`, *optional*, defaults to `1.0`):
            Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far.
            Values > `1.0` encourage the model to use new tokens, while values < `1.0` encourage the model to repeat
            tokens.
        cache_implementation (`str`, *optional*):
            Implementation of the cache method for faster generation when `use_vllm` is set to `False`.

        > Parameters that control generation acceleration powered by vLLM

        use_vllm (`bool`, *optional*, defaults to `False`):
            Whether to use vLLM for generating completions. If set to `True`, the trainer will use vLLM for generation
            instead of the default model.generate(). Requires `vllm` to be installed.
        vllm_mode (`str`, *optional*, defaults to `"colocate"`):
            Mode to use for vLLM integration when `use_vllm` is set to `True`. Must be one of `"server"` or
            `"colocate"`.

            - `"server"`: The trainer will send generation requests to a separate vLLM server. Make sure a TRL vLLM
              server is running (start with `trl vllm-serve`).
            - `"colocate"`: vLLM will run in the same process and share the training GPUs. This avoids the need for a
              separate server but may cause resource contention with training.
        vllm_model_impl (`str`, *optional*, defaults to `"vllm"`):
            Model implementation to use for vLLM. Must be one of `"transformers"` or `"vllm"`. `"transformers"`: Use
            the `transformers` backend for model implementation. `"vllm"`: Use the `vllm` library for model
            implementation.
        vllm_structured_outputs_regex (`str`, *optional*):
            Regex for vLLM structured outputs. If `None` (default), structured outputs is disabled.

        > Parameters that control the vLLM server (only used when `vllm_mode` is `"server"`)

        vllm_server_base_url (`str`, *optional*):
            Base URL for the vLLM server (e.g., `"http://localhost:8000"`). If provided, `vllm_server_host` and
            `vllm_server_port` are ignored.
        vllm_server_host (`str`, *optional*, defaults to `"0.0.0.0"`):
            Host of the vLLM server to connect to. Ignored if `vllm_server_base_url` is provided.
        vllm_server_port (`int`, *optional*, defaults to `8000`):
            Port of the vLLM server to connect to. Ignored if `vllm_server_base_url` is provided.
        vllm_server_timeout (`float`, *optional*, defaults to `240.0`):
            Total timeout duration in seconds to wait for the vLLM server to be up. If the server is not up after the
            timeout, a `ConnectionError` is raised.
        vllm_group_port (`int`, *optional*, defaults to `51216`):
            Port number for the weight update group. This is used to communicate with the vLLM server. Unless the port
            is occupied, there is no need to change it.

        > Parameters that control colocated vLLM execution (only used when `vllm_mode` is `"colocate"`)

        vllm_gpu_memory_utilization (`float`, *optional*, defaults to `0.3`):
            Control the GPU memory utilization for vLLM. This setting only applies when `vllm_mode` is set to
            `"colocate"`. If you are using `vllm_mode="server"`, this parameter must be passed separately when
            launching the vLLM server via the `--vllm_gpu_memory_utilization` flag.
        vllm_max_model_length (`int`, *optional*):
            Context window for vLLM. Set it to at least the maximum prompt length in the dataset plus
            `max_completion_length`; if omitted, it is inferred from the model config.
        vllm_tensor_parallel_size (`int`, *optional*, defaults to `1`):
            Control the tensor parallel size for vLLM. This setting only applies when `vllm_mode` is set to
            `"colocate"`. If you are using `vllm_mode="server"`, this parameter must be passed separately when
            launching the vLLM server via the `--vllm_tensor_parallel_size` flag.
        vllm_enable_sleep_mode (`bool`, *optional*, defaults to `False`):
            Enable vLLM sleep mode to offload weights/cache during the optimizer step. Keeps GPU memory usage low, but
            waking the engine adds host–device transfer latency.

        > Parameters that control generation acceleration powered by transformers continuous batching

        use_transformers_continuous_batching (`bool`, *optional*, defaults to `False`):
            Whether to use transformers' continuous batching engine for generating completions. Requires
            `transformers>=5.8.0`.
        transformers_continuous_batching_config (`dict`, *optional*):
            Keyword arguments for [`~transformers.generation.ContinuousBatchingConfig`].

        > Parameters that control the training

        beta (`float`, *optional*, defaults to `0.0`):
            KL coefficient. If `0.0` (default), the reference model is not loaded, reducing memory usage and improving
            training speed. [DeepSeek-R1 incentivizes reasoning in LLMs through reinforcement
            learning](https://huggingface.co/papers/2501.12948) use a value of `0.001`.
        num_iterations (`int`, *optional*, defaults to `1`):
            Number of iterations per batch (denoted as μ in the algorithm).
        epsilon (`float`, *optional*, defaults to `0.2`):
            Epsilon value for clipping.
        delta (`float`, *optional*):
            Enables the upper clipping bound in two-sided GRPO loss when set to a float. If `None` (default), standard
            GRPO clipping is used. Recommended to be greater than `1 + ε` when enabled. This method is introduced in
            the [INTELLECT-2 tech report](https://huggingface.co/papers/2505.07291).
        epsilon_high (`float`, *optional*):
            Upper-bound epsilon value for clipping. If not specified, it defaults to the same value as the lower-bound
            specified in argument `epsilon`. Paper [DAPO](https://huggingface.co/papers/2503.14476) recommends `0.28`.
            When used with `loss_type='cispo'`, this corresponds to the ε_max param specified in the [ScaleRL
            paper](https://huggingface.co/papers/2510.13786) and the recommended value is `5.0`.
        sapo_temperature_neg (`float`, *optional*, defaults to `1.05`):
            Temperature for tokens with non-positive advantage scores used in the `sapo` loss function. This parameter
            is introduced in the [Soft Adaptive Policy Optimization paper](https://huggingface.co/papers/2511.20347).
        sapo_temperature_pos (`float`, *optional*, defaults to `1.0`):
            Temperature for tokens with positive advantage scores used in the `sapo` loss function. This parameter is
            introduced in the [Soft Adaptive Policy Optimization paper](https://huggingface.co/papers/2511.20347).
        vespo_k_pos (`float`, *optional*, defaults to `2.0`):
            k parameter for positive advantages, it is the power exponent in the VESPO loss. Controls how aggressively
            we down-weight samples with low importance weights (when the importance sampling ratio < 1).
        vespo_lambda_pos (`float`, *optional*, defaults to `3.0`):
            lambda parameter for positive advantages, it is the decay factor in the VESPO loss. Controls how
            aggressively we down-weight samples with high importance weights (when the importance sampling ratio > 1).
        vespo_k_neg (`float`, *optional*, defaults to `3.0`):
            k parameter for negative advantages, it is the power exponent in the VESPO loss. Controls how aggressively
            we down-weight samples with low importance weights (when the importance sampling ratio < 1).
        vespo_lambda_neg (`float`, *optional*, defaults to `2.0`):
            lambda parameter for negative advantages, it is the exponential decay factor in the VESPO loss. Controls
            how aggressively we down-weight samples with high importance weights (when the importance sampling ratio >
            1).
        importance_sampling_level (`str`, *optional*, defaults to `"token"`):
            Controls whether importance sampling ratios are computed at the `"token"` or `"sequence"` level. `"token"`
            keeps the raw per-token log-probability ratios (one weight per token). `"sequence"` averages the
            log-probability ratios across valid tokens to produce a single ratio per sequence. The [GSPO
            paper](https://huggingface.co/papers/2507.18071) shows that sequence-level sampling often yields more
            stable training and better alignment with sequence-level rewards.
        reward_weights (`list[float]`, *optional*):
            Weights for each reward function. Must match the number of reward functions. If `None`, all rewards are
            weighted equally with weight `1.0`.
        multi_objective_aggregation (`str`, *optional*, defaults to `"sum_then_normalize"`):
            Method to aggregate multiple reward functions. Supported values are:

            - `"sum_then_normalize"` (default): First sums the weighted rewards from each reward function, then applies
              reward scaling/normalization as specified by `scale_rewards` (see `scale_rewards` for details).
            - `"normalize_then_sum"`: First normalizes/scales each reward function across generations (within each
              group), then sums the normalized rewards using the specified weights. The aggregated reward is then
              normalized at the batch level when forming advantages. This is the suggested approach from the paper
              [GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL
              Optimization](https://huggingface.co/papers/2601.05242).
        scale_rewards (`str` or `bool`, *optional*, defaults to `"group"`):
            Specifies the scaling strategy for rewards. Supported values are:

            - `True` or `"group"` (default): rewards are scaled by the standard deviation within each group, ensuring
              unit variance within a group.
            - `"batch"`: rewards are scaled by the standard deviation across the entire batch, as recommended in the
              [PPO Lite paper](https://huggingface.co/papers/2508.08221).
            - `False` or `"none"`: no scaling is applied. The [Dr. GRPO
              paper](https://huggingface.co/papers/2503.20783) recommends not scaling rewards, as scaling by the
              standard deviation introduces a question-level difficulty bias.
        loss_type (`str`, *optional*, defaults to `"dapo"`):
            Specifies the loss formulation to use. Supported values are:

            - `"grpo"`: Aggregates token-level losses by normalizing over sequence length. Not recommended due to
              length bias—this approach tends to prefer shorter completions with positive advantages and longer ones
              with negative advantages.
            - `"dr_grpo"`: Aggregates token-level losses by normalizing with a global constant. This method was
              introduced in the [Dr. GRPO paper](https://huggingface.co/papers/2503.20783) to eliminate length bias.
              The value of the constant corresponds to `max_completion_length`.
            - `"dapo"` (default): Aggregates token-level losses by normalizing with the number of active token in the
              global accumulated batch. This method was introduced in the [DAPO
              paper](https://huggingface.co/papers/2503.14476) to eliminate length bias.
            - `"bnpo"`: Aggregates token-level losses by normalizing with the number of active token in the local
              batch. Note that normalization is performed over the local batch only, so results may slightly vary
              depending on the local batch size, despite a constant effective batch size. When using
              `per_device_train_batch_size==1`, the loss is equivalent to the GRPO loss.
            - `"cispo"`: Clips the importance sampling weights instead of the advantage scaled importance weights. The
              clipped weights are then multiplied with the advantages and policy model's log probs. Individual token
              losses are aggregated by normalizing with the number of active tokens in the global accumulated batch.
              This method was introduced in the [MiniMax-M1 paper](https://huggingface.co/papers/2506.13585).
            - `"sapo"`: Soft Adaptive Policy Optimization loss, as introduced in the [Soft Adaptive Policy Optimization
              paper](https://huggingface.co/papers/2511.20347). Replaces hard clipping with a smooth,
              temperature-controlled gate that adaptively attenuates off-policy updates while preserving useful
              learning signals.
            - `"luspo"`: Length-Unbiased Sequence Policy Optimization loss. A sequence-level loss that scales each
              sequence's loss by its length. This is a modification of GSPO and requires
              `importance_sampling_level="sequence"`. Introduced in the [LUSPO
              paper](https://huggingface.co/papers/2602.05261).
            - `"vespo"`: Variational Sequence-Level Soft Policy Optimization. Replaces hard clipping with a smooth,
              asymmetric Gamma weighting function applied directly to sequence-level importance weights. Introduced in
              the [VESPO paper](https://huggingface.co/papers/2602.10693).
        mask_truncated_completions (`bool`, *optional*, defaults to `False`):
            When enabled, truncated completions are excluded from the loss calculation, preventing them from being
            incorrectly penalized and introducing noise during training. According to the
            [DAPO](https://huggingface.co/papers/2503.14476) paper, this is a good practice for training stability.
        sync_ref_model (`bool`, *optional*, defaults to `False`):
            Whether to synchronize the reference model with the active model every `ref_model_sync_steps` steps, using
            the `ref_model_mixup_alpha` parameter. This synchronization originates from the
            [TR-DPO](https://huggingface.co/papers/2404.09656) paper.
        ref_model_mixup_alpha (`float`, *optional*, defaults to `0.6`):
            α parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which controls the mix
            between the current policy and the previous reference policy during updates. The reference policy is
            updated according to the equation: `π_ref = α * π_θ + (1 - α) * π_ref_prev`. To use this parameter, you
            must set `sync_ref_model=True`.
        ref_model_sync_steps (`int`, *optional*, defaults to `512`):
            τ parameter from the [TR-DPO](https://huggingface.co/papers/2404.09656) paper, which determines how
            frequently the current policy is synchronized with the reference policy. To use this parameter, you must
            set `sync_ref_model=True`.
        top_entropy_quantile (`float`, *optional*, defaults to `1.0`):
            ρ parameter from [Beyond the 80/20 Rule](https://huggingface.co/papers/2506.01939). Keeps in the policy
            loss term only the top-ρ quantile of tokens by entropy of the probability distribution at each sequence
            position, improving results. Range: `[0.0-1.0]`. A value of `0.0` masks all but the highest entropy token;
            `1.0` keeps all tokens. The paper recommends a value of `0.2`. If used with
            `mask_truncated_completions=True`, only tokens from non-truncated completions are considered.
        max_tool_calling_iterations (`int`, *optional*):
            Maximum number of tool-calling turns when training an agent. If `None`, there is no limit and generation
            stops when the model generates a response turn with no tool calls or when the total response length reaches
            `max_model_length`.
        vllm_importance_sampling_correction (`bool`, *optional*, defaults to `True`):
            Whether to apply Importance Sampling (IS) to correct for the mismatch between vLLM completion logprobs and
            recomputed training logprobs. If set to `False`, no IS is applied regardless of
            `vllm_importance_sampling_mode`. When `True`, the selected mode determines how the IS ratios are computed
            and constrained.
        vllm_importance_sampling_mode (`str`, *optional*, defaults to `"sequence_mask"`):
            Specifies how Importance Sampling is performed when `vllm_importance_sampling_correction=True`. Possible
            values are:

                - `"token_truncate"`: Token-level truncated IS (default). Per-token ratios are clipped to
                [C_min, C_max].
                - `"token_mask"`: Token-level masked IS. Per-token ratios outside [C_min, C_max] are set to zero.
                - `"sequence_truncate"`: Sequence-level truncated IS. A single sequence ratio is clipped to
                [C_min, C_max] and applied to all tokens in the sequence.
                - `"sequence_mask"`: Sequence-level masked IS. Sequences with ratios outside [C_min, C_max] are masked
                out.
        vllm_importance_sampling_clip_max (`float`, *optional*, defaults to `3.0`):
            Importance sampling upper bound C_max used by `vllm_importance_sampling_mode`. For `*_truncate` modes,
            importance ratios are clipped from above at C_max. For `*_mask` modes, ratios larger than C_max are set to
            zero.
        vllm_importance_sampling_clip_min (`float`, *optional*):
            Importance sampling lower bound C_min used by `vllm_importance_sampling_mode`. For `*_truncate` modes,
            ratios are clipped from below at C_min. For `*_mask` modes, ratios below C_min are set to zero. To strictly
            mask ratios below C_min without upper bound, set `vllm_importance_sampling_clip_max=None`.
        off_policy_mask_threshold (`float`, *optional*):
            Threshold for off-policy sequence masking. If `None`, off-policy sequence masking is disabled. When set,
            sequences with negative advantages and high KL divergence are masked out to stabilize training. This
            parameter corresponds to the `delta` threshold in Equation 9 of the [DeepSeek-V3.2
            paper](https://huggingface.co/papers/2512.02556). It expects a positive value (e.g., 0.5).
        use_bias_correction_kl (`bool`, *optional*, defaults to `False`):
            Whether to use the unbiased KL divergence estimator with importance sampling correction. This corrects the
            KL divergence estimate by multiplying it with the importance sampling ratio. This is described in the
            [DeepSeek-V3.2 paper](https://huggingface.co/papers/2512.02556).

        > Parameters that control the logging

        log_completions (`bool`, *optional*, defaults to `False`):
            Whether to log a sample of (prompt, completion) pairs every `logging_steps` steps. If `rich` is installed,
            it prints the sample. If `wandb` and/or `trackio` logging is enabled, it logs it to `wandb` and/or
            `trackio`.
        num_completions_to_print (`int`, *optional*):
            Number of completions to print with `rich`. If `None`, all completions are logged.
        log_unique_prompts (`bool`, *optional*, defaults to `False`):
            Whether to log unique prompts. If `True`, only unique prompts are logged. If `False`, all prompts are
            logged.
        log_completions_hub_repo (`str`, *optional*):
            Hugging Face Hub repository to save the completions. Should be a complete repository name like
            `'username/reponame'` or `'orgname/reponame'`, or just `'reponame'` in which case the repository will be
            created in the currently-logged-in Hugging Face user's namespace. Note that this repository will be public
            unless you set `hub_private_repo=True` or your organization's default is to create private repositories."

        > Deprecated parameters

        use_transformers_paged:

            <Deprecated version="1.2.0">

            Parameter `use_transformers_paged` is deprecated and will be removed in version v2.0.0. Use
            `use_transformers_continuous_batching` instead.

            </Deprecated>

        vllm_importance_sampling_cap:

            <Deprecated version="1.6.0">

            Parameter `vllm_importance_sampling_cap` is deprecated and will be removed in v2.0.0. Use
            `vllm_importance_sampling_clip_max` instead.

            </Deprecated>

    > [!NOTE]
    > These parameters have default values different from [`~transformers.TrainingArguments`]:
    > - `logging_steps`: Defaults to `10` instead of `500`.
    > - `gradient_checkpointing`: Defaults to `True` instead of `False`.
    > - `bf16`: Defaults to `True` if `fp16` is not set, instead of `False`.
    > - `learning_rate`: Defaults to `1e-6` instead of `5e-5`.
    
    """
    vllm_sampling_params: Optional[Any] = field(
        default = None,
        metadata = {'help': 'vLLM SamplingParams'},
    )
    unsloth_num_chunks : Optional[int] = field(
        default = -1,
        metadata = {'help': 'Chunk size to reduce memory usage. -1 is most efficient.'},
    )
    unsloth_logit_chunk_multiplier : Optional[int] = field(
            default = None,
            metadata = {'help': 'Multiplier for chunked logit computations.'},
        )
    unsloth_grpo_mini_batch : Optional[int] = field(
        default = None,
        metadata = {'help': 'Mini batch size for GRPO hidden state accumulation. Default is None unless user defines it.'},
    )
    
    def __init__(
        self,
        output_dir = None,
        per_device_train_batch_size = 4,
        num_train_epochs = 3.0,
        max_steps = -1,
        learning_rate = 5e-05,
        lr_scheduler_type = 'linear',
        lr_scheduler_kwargs = None,
        warmup_steps = 0.1,
        optim = 'adamw_8bit',
        optim_args = None,
        weight_decay = 0.001,
        adam_beta1 = 0.9,
        adam_beta2 = 0.999,
        adam_epsilon = 1e-08,
        optim_target_modules = None,
        gradient_accumulation_steps = 2,
        average_tokens_across_devices = True,
        max_grad_norm = 1.0,
        label_smoothing_factor = 0.0,
        bf16 = False,
        fp16 = False,
        bf16_full_eval = False,
        fp16_full_eval = False,
        tf32 = None,
        gradient_checkpointing = True,
        gradient_checkpointing_kwargs = None,
        torch_compile = False,
        torch_compile_backend = None,
        torch_compile_mode = None,
        use_liger_kernel = False,
        liger_kernel_config = None,
        use_cache = False,
        neftune_noise_alpha = None,
        torch_empty_cache_steps = 250,
        auto_find_batch_size = False,
        logging_strategy = 'steps',
        logging_steps = 1,
        logging_first_step = False,
        log_on_each_node = True,
        logging_nan_inf_filter = False,
        include_num_input_tokens_seen = False,
        log_level = 'passive',
        log_level_replica = 'warning',
        disable_tqdm = None,
        report_to = 'none',
        run_name = None,
        project = 'huggingface',
        trackio_space_id = 'trackio',
        eval_strategy = 'no',
        eval_steps = None,
        eval_delay = 0,
        per_device_eval_batch_size = 4,
        prediction_loss_only = False,
        eval_on_start = False,
        eval_do_concat_batches = True,
        eval_use_gather_object = False,
        eval_accumulation_steps = 2,
        batch_eval_metrics = False,
        save_only_model = False,
        save_strategy = 'steps',
        save_steps = 500,
        save_on_each_node = False,
        save_total_limit = None,
        enable_jit_checkpoint = False,
        push_to_hub = False,
        hub_token = None,
        hub_private_repo = None,
        hub_model_id = None,
        hub_strategy = 'every_save',
        hub_always_push = False,
        hub_revision = None,
        load_best_model_at_end = False,
        metric_for_best_model = None,
        greater_is_better = None,
        ignore_data_skip = False,
        restore_callback_states_from_checkpoint = False,
        full_determinism = False,
        seed = 3407,
        data_seed = 3407,
        use_cpu = False,
        accelerator_config = None,
        parallelism_config = None,
        dataloader_drop_last = False,
        dataloader_num_workers = 0,
        dataloader_pin_memory = True,
        dataloader_persistent_workers = False,
        dataloader_prefetch_factor = None,
        remove_unused_columns = False,
        label_names = None,
        train_sampling_strategy = 'random',
        length_column_name = 'length',
        ddp_find_unused_parameters = None,
        ddp_bucket_cap_mb = None,
        ddp_broadcast_buffers = None,
        ddp_backend = None,
        ddp_timeout = 1800,
        fsdp = None,
        fsdp_config = None,
        deepspeed = None,
        debug = '',
        skip_memory_metrics = True,
        do_train = False,
        do_eval = False,
        do_predict = False,
        resume_from_checkpoint = None,
        warmup_ratio = None,
        logging_dir = None,
        local_rank = -1,
        model_init_kwargs = None,
        trust_remote_code = False,
        router_aux_loss_coef = 0.001,
        disable_dropout = False,
        cast_lm_head_to_fp32 = False,
        num_generations = 8,
        num_generations_eval = None,
        max_completion_length = 256,
        ds3_gather_for_generation = True,
        shuffle_dataset = True,
        pad_to_multiple_of = None,
        generation_batch_size = None,
        steps_per_generation = None,
        temperature = 1.0,
        top_p = 1.0,
        top_k = None,
        min_p = None,
        generation_kwargs = {},
        chat_template_kwargs = None,
        repetition_penalty = 1.0,
        cache_implementation = None,
        use_vllm = False,
        vllm_mode = 'colocate',
        vllm_model_impl = 'vllm',
        vllm_enable_sleep_mode = False,
        vllm_structured_outputs_regex = None,
        vllm_server_base_url = None,
        vllm_server_host = '0.0.0.0',
        vllm_server_port = 8000,
        vllm_server_timeout = 240.0,
        vllm_group_port = 51216,
        vllm_gpu_memory_utilization = 0.3,
        vllm_max_model_length = None,
        vllm_tensor_parallel_size = 1,
        beta = 0.001,
        num_iterations = 1,
        epsilon = 0.2,
        delta = None,
        epsilon_high = None,
        sapo_temperature_neg = 1.05,
        sapo_temperature_pos = 1.0,
        vespo_k_pos = 2.0,
        vespo_lambda_pos = 3.0,
        vespo_k_neg = 3.0,
        vespo_lambda_neg = 2.0,
        importance_sampling_level = 'token',
        reward_weights = None,
        multi_objective_aggregation = 'sum_then_normalize',
        scale_rewards = 'group',
        loss_type = 'bnpo',
        mask_truncated_completions = False,
        sync_ref_model = False,
        ref_model_mixup_alpha = 0.6,
        ref_model_sync_steps = 512,
        top_entropy_quantile = 1.0,
        max_tool_calling_iterations = None,
        vllm_importance_sampling_correction = False,
        vllm_importance_sampling_mode = 'sequence_mask',
        vllm_importance_sampling_clip_max = 3.0,
        vllm_importance_sampling_clip_min = None,
        off_policy_mask_threshold = None,
        use_bias_correction_kl = False,
        log_completions = False,
        num_completions_to_print = None,
        log_unique_prompts = False,
        log_completions_hub_repo = None,
        use_transformers_continuous_batching = False,
        transformers_continuous_batching_config = None,
        use_transformers_paged = False,
        vllm_importance_sampling_cap = None,
        vllm_sampling_params = None,
        unsloth_num_chunks = -1,
        unsloth_logit_chunk_multiplier = None,
        unsloth_grpo_mini_batch = None,
        
        **kwargs,
    ):
        if learning_rate < 1e-7: print(f'Unsloth: Your learning rate of `{learning_rate}` is too small and less than 1e-7! Consider increasing it, otherwise gradient updates will be close to 0!')
        if learning_rate > 1: print(f'Unsloth: Your learning rate of `{learning_rate}` is way too larger > 1! Consider decreasing it to 1e-1, otherwise gradient updates will explode!')
        if num_train_epochs is None:
            num_train_epochs = 3.0  # Default to 3 epochs if None, max_steps will override
        if output_dir is None and save_strategy == 'steps' and save_steps == 500:
            output_dir = 'unsloth_training_checkpoints'
            save_strategy = 'no'
        if os.environ.get('UNSLOTH_ENABLE_FLEX_ATTENTION', '0') == '1':
            from unsloth_zoo.flex_attention import HAS_FLEX_ATTENTION
            if HAS_FLEX_ATTENTION and pad_to_multiple_of is None:
                from unsloth_zoo.flex_attention import FLEX_ATTENTION_BLOCK_SIZE
                pad_to_multiple_of = FLEX_ATTENTION_BLOCK_SIZE
        
        if loss_type.lower() == 'dr_grpo':
            loss_type = 'dr_grpo'
        elif loss_type.lower() == 'dapo':
            loss_type = 'dapo'
        if loss_type.lower() == 'dr_grpo':
            if scale_rewards == None:
                scale_rewards = True
            elif scale_rewards == True:
                print('Unsloth: The Dr GRPO paper recommends setting `scale_rewards` to False! Will override. Set it to `None` to force False.')
                scale_rewards = False
        elif loss_type.lower() == 'dapo':
            if mask_truncated_completions != True:
                print('Unsloth: The DAPO paper recommends `mask_truncated_completions = True` - we will set it.')
            if epsilon_high != 0.28:
                print('Unsloth: The DAPO paper recommends `epsilon_high = 0.28` - we will set it.')
            if beta != 0.0:
                print(f'[WARNING] Unsloth: The DAPO paper recommends setting `beta = 0.0` to remove the KL term - You have set it to {beta}.')
            mask_truncated_completions = True
            epsilon_high = 0.28
        
        if steps_per_generation is None and generation_batch_size is None:
            ga = gradient_accumulation_steps
            world_size = int(os.environ.get('WORLD_SIZE', '1'))
            if (ga * world_size * per_device_train_batch_size) % num_generations != 0:
                print('Unsloth: We now expect `per_device_train_batch_size` * `gradient_accumulation_steps` * `world_size` to be a multiple of `num_generations`.\nWe will change the batch size of ' + str(per_device_train_batch_size) + ' to the `num_generations` of ' + str(num_generations))
                per_device_train_batch_size = num_generations
        
        if temperature <= 0:
            raise ValueError('Unsloth: Please set a positive non-zero temperature since your results will be wrong.')
        elif temperature >= 10:
            raise ValueError('Unsloth: Please set a positive non-zero temperature less than 10, since sampling will be quite erratic.')
        
        if use_vllm and (top_k is None or top_k == 0): top_k = -1
        
        super().__init__(
            output_dir = output_dir,
            per_device_train_batch_size = per_device_train_batch_size,
            num_train_epochs = num_train_epochs,
            max_steps = max_steps,
            learning_rate = learning_rate,
            lr_scheduler_type = lr_scheduler_type,
            lr_scheduler_kwargs = lr_scheduler_kwargs,
            warmup_steps = warmup_steps,
            optim = optim,
            optim_args = optim_args,
            weight_decay = weight_decay,
            adam_beta1 = adam_beta1,
            adam_beta2 = adam_beta2,
            adam_epsilon = adam_epsilon,
            optim_target_modules = optim_target_modules,
            gradient_accumulation_steps = gradient_accumulation_steps,
            average_tokens_across_devices = average_tokens_across_devices,
            max_grad_norm = max_grad_norm,
            label_smoothing_factor = label_smoothing_factor,
            bf16 = bf16,
            fp16 = fp16,
            bf16_full_eval = bf16_full_eval,
            fp16_full_eval = fp16_full_eval,
            tf32 = tf32,
            gradient_checkpointing = gradient_checkpointing,
            gradient_checkpointing_kwargs = gradient_checkpointing_kwargs,
            torch_compile = torch_compile,
            torch_compile_backend = torch_compile_backend,
            torch_compile_mode = torch_compile_mode,
            use_liger_kernel = use_liger_kernel,
            liger_kernel_config = liger_kernel_config,
            use_cache = use_cache,
            neftune_noise_alpha = neftune_noise_alpha,
            torch_empty_cache_steps = torch_empty_cache_steps,
            auto_find_batch_size = auto_find_batch_size,
            logging_strategy = logging_strategy,
            logging_steps = logging_steps,
            logging_first_step = logging_first_step,
            log_on_each_node = log_on_each_node,
            logging_nan_inf_filter = logging_nan_inf_filter,
            include_num_input_tokens_seen = include_num_input_tokens_seen,
            log_level = log_level,
            log_level_replica = log_level_replica,
            disable_tqdm = disable_tqdm,
            report_to = report_to,
            run_name = run_name,
            project = project,
            trackio_space_id = trackio_space_id,
            eval_strategy = eval_strategy,
            eval_steps = eval_steps,
            eval_delay = eval_delay,
            per_device_eval_batch_size = per_device_eval_batch_size,
            prediction_loss_only = prediction_loss_only,
            eval_on_start = eval_on_start,
            eval_do_concat_batches = eval_do_concat_batches,
            eval_use_gather_object = eval_use_gather_object,
            eval_accumulation_steps = eval_accumulation_steps,
            batch_eval_metrics = batch_eval_metrics,
            save_only_model = save_only_model,
            save_strategy = save_strategy,
            save_steps = save_steps,
            save_on_each_node = save_on_each_node,
            save_total_limit = save_total_limit,
            enable_jit_checkpoint = enable_jit_checkpoint,
            push_to_hub = push_to_hub,
            hub_token = hub_token,
            hub_private_repo = hub_private_repo,
            hub_model_id = hub_model_id,
            hub_strategy = hub_strategy,
            hub_always_push = hub_always_push,
            hub_revision = hub_revision,
            load_best_model_at_end = load_best_model_at_end,
            metric_for_best_model = metric_for_best_model,
            greater_is_better = greater_is_better,
            ignore_data_skip = ignore_data_skip,
            restore_callback_states_from_checkpoint = restore_callback_states_from_checkpoint,
            full_determinism = full_determinism,
            seed = seed,
            data_seed = data_seed,
            use_cpu = use_cpu,
            accelerator_config = accelerator_config,
            parallelism_config = parallelism_config,
            dataloader_drop_last = dataloader_drop_last,
            dataloader_num_workers = dataloader_num_workers,
            dataloader_pin_memory = dataloader_pin_memory,
            dataloader_persistent_workers = dataloader_persistent_workers,
            dataloader_prefetch_factor = dataloader_prefetch_factor,
            remove_unused_columns = remove_unused_columns,
            label_names = label_names,
            train_sampling_strategy = train_sampling_strategy,
            length_column_name = length_column_name,
            ddp_find_unused_parameters = ddp_find_unused_parameters,
            ddp_bucket_cap_mb = ddp_bucket_cap_mb,
            ddp_broadcast_buffers = ddp_broadcast_buffers,
            ddp_backend = ddp_backend,
            ddp_timeout = ddp_timeout,
            fsdp = fsdp,
            fsdp_config = fsdp_config,
            deepspeed = deepspeed,
            debug = debug,
            skip_memory_metrics = skip_memory_metrics,
            do_train = do_train,
            do_eval = do_eval,
            do_predict = do_predict,
            resume_from_checkpoint = resume_from_checkpoint,
            warmup_ratio = warmup_ratio,
            logging_dir = logging_dir,
            local_rank = local_rank,
            model_init_kwargs = model_init_kwargs,
            trust_remote_code = trust_remote_code,
            router_aux_loss_coef = router_aux_loss_coef,
            disable_dropout = disable_dropout,
            cast_lm_head_to_fp32 = cast_lm_head_to_fp32,
            num_generations = num_generations,
            num_generations_eval = num_generations_eval,
            max_completion_length = max_completion_length,
            ds3_gather_for_generation = ds3_gather_for_generation,
            shuffle_dataset = shuffle_dataset,
            pad_to_multiple_of = pad_to_multiple_of,
            generation_batch_size = generation_batch_size,
            steps_per_generation = steps_per_generation,
            temperature = temperature,
            top_p = top_p,
            top_k = top_k,
            min_p = min_p,
            generation_kwargs = generation_kwargs,
            chat_template_kwargs = chat_template_kwargs,
            repetition_penalty = repetition_penalty,
            cache_implementation = cache_implementation,
            use_vllm = use_vllm,
            vllm_mode = vllm_mode,
            vllm_model_impl = vllm_model_impl,
            vllm_enable_sleep_mode = vllm_enable_sleep_mode,
            vllm_structured_outputs_regex = vllm_structured_outputs_regex,
            vllm_server_base_url = vllm_server_base_url,
            vllm_server_host = vllm_server_host,
            vllm_server_port = vllm_server_port,
            vllm_server_timeout = vllm_server_timeout,
            vllm_group_port = vllm_group_port,
            vllm_gpu_memory_utilization = vllm_gpu_memory_utilization,
            vllm_max_model_length = vllm_max_model_length,
            vllm_tensor_parallel_size = vllm_tensor_parallel_size,
            beta = beta,
            num_iterations = num_iterations,
            epsilon = epsilon,
            delta = delta,
            epsilon_high = epsilon_high,
            sapo_temperature_neg = sapo_temperature_neg,
            sapo_temperature_pos = sapo_temperature_pos,
            vespo_k_pos = vespo_k_pos,
            vespo_lambda_pos = vespo_lambda_pos,
            vespo_k_neg = vespo_k_neg,
            vespo_lambda_neg = vespo_lambda_neg,
            importance_sampling_level = importance_sampling_level,
            reward_weights = reward_weights,
            multi_objective_aggregation = multi_objective_aggregation,
            scale_rewards = scale_rewards,
            loss_type = loss_type,
            mask_truncated_completions = mask_truncated_completions,
            sync_ref_model = sync_ref_model,
            ref_model_mixup_alpha = ref_model_mixup_alpha,
            ref_model_sync_steps = ref_model_sync_steps,
            top_entropy_quantile = top_entropy_quantile,
            max_tool_calling_iterations = max_tool_calling_iterations,
            vllm_importance_sampling_correction = vllm_importance_sampling_correction,
            vllm_importance_sampling_mode = vllm_importance_sampling_mode,
            vllm_importance_sampling_clip_max = vllm_importance_sampling_clip_max,
            vllm_importance_sampling_clip_min = vllm_importance_sampling_clip_min,
            off_policy_mask_threshold = off_policy_mask_threshold,
            use_bias_correction_kl = use_bias_correction_kl,
            log_completions = log_completions,
            num_completions_to_print = num_completions_to_print,
            log_unique_prompts = log_unique_prompts,
            log_completions_hub_repo = log_completions_hub_repo,
            use_transformers_continuous_batching = use_transformers_continuous_batching,
            transformers_continuous_batching_config = transformers_continuous_batching_config,
            use_transformers_paged = use_transformers_paged,
            vllm_importance_sampling_cap = vllm_importance_sampling_cap,**kwargs)
        self.vllm_sampling_params = vllm_sampling_params
        self.unsloth_num_chunks = unsloth_num_chunks
        if unsloth_grpo_mini_batch is not None:
            if self.generation_batch_size >= unsloth_grpo_mini_batch:
                self.unsloth_grpo_mini_batch = unsloth_grpo_mini_batch
            else:
                raise ValueError(
                    f"Unsloth GRPO mini batch size needs to be less than or equal to the effective generation batch size, "
                    f"which is self.per_device_train_batch_size * gradient_accumulation_steps."
                )
        self.unsloth_logit_chunk_multiplier = unsloth_logit_chunk_multiplier
        
        # Unsloth: Remove use_reentrant=False forced by TRL 0.27.0+
        if getattr(self, 'gradient_checkpointing_kwargs', None) is not None:
            if 'use_reentrant' in self.gradient_checkpointing_kwargs:
                del self.gradient_checkpointing_kwargs['use_reentrant']

pass

class _UnslothGRPOTrainer(_BaseTrainer):
    """"""

    _tag_names = ["trl", "grpo"]
    _name = "GRPO"
    _paper = {
        "title": "DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models",
        "id": "2402.03300",
        # docstyle-ignore
        "citation": textwrap.dedent("""\
            @article{shao2024deepseekmath,
                title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
                author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
                year         = 2024,
                eprint       = {arXiv:2402.03300},
            }"""),
    }

    def __init__(
        self,
        model: "str | PreTrainedModel | PeftModel",
        reward_funcs: RewardFunc | list[RewardFunc],
        args: GRPOConfig | None = None,
        train_dataset: Dataset | IterableDataset | None = None,
        eval_dataset: Dataset | IterableDataset | dict[str, Dataset | IterableDataset] | None = None,
        processing_class: PreTrainedTokenizerBase | ProcessorMixin | None = None,
        reward_processing_classes: PreTrainedTokenizerBase | list[PreTrainedTokenizerBase] | None = None,
        callbacks: list[TrainerCallback] | None = None,
        optimizers: tuple[torch.optim.Optimizer | None, torch.optim.lr_scheduler.LambdaLR | None] = (None, None),
        peft_config: "PeftConfig | None" = None,
        tools: list[Callable] | None = None,
        rollout_func: RolloutFunc | None = None,
        environment_factory: EnvironmentFactory | None = None,
    ):

        if hasattr(model, 'vllm_engine') and hasattr(args, 'use_vllm'):
            if (getattr(args, 'use_vllm', False) == False):
                args.use_vllm = True
            args.vllm_mode='colocate'
            _unsloth_esm = getattr(getattr(getattr(getattr(model.vllm_engine, 'llm_engine', None), 'vllm_config', None), 'model_config', None), 'enable_sleep_mode', None)
            if (_unsloth_esm if _unsloth_esm is not None else os.environ.get('UNSLOTH_VLLM_STANDBY', '0') != '0'):
                args.vllm_enable_sleep_mode=True
        # Args
        if args is None:
            model_name = model if isinstance(model, str) else get_config_model_id(model.config)
            model_name = model_name.split("/")[-1]
            args = GRPOConfig(f"{model_name}-GRPO")

        # Model
        if isinstance(model, str):
            model_init_kwargs = args.model_init_kwargs or {}
            # Distributed training requires device_map=None ["auto" fails]
            if args.distributed_state.distributed_type in ["MULTI_GPU", "DEEPSPEED"]:
                model_init_kwargs["device_map"] = None
            model_init_kwargs.setdefault("trust_remote_code", args.trust_remote_code)
            model = create_model_from_path(model, **model_init_kwargs)
        else:
            if args.model_init_kwargs is not None:
                logger.warning(
                    "You passed `model_init_kwargs` to the `GRPOConfig`, but your model is already instantiated. "
                    "The `model_init_kwargs` will be ignored."
                )
        # Non-quantized models do not have the `is_loaded_in_{8,4}bit` attributes, whereas quantized models do
        _is_quantized_model = getattr(model, "is_loaded_in_4bit", False) or getattr(model, "is_loaded_in_8bit", False)

        # Some models [SmolVLM/Idefics3] don't support `logits_to_keep` argument and error out if we pass it
        # Inspect the forward method before we wrap the model with PEFT
        self.model_kwarg_keys = (
            inspect.signature(model.forward).parameters.keys()
            if not hasattr(model, "get_base_model")
            else inspect.signature(model.get_base_model().forward).parameters.keys()
        )

        # Processing class
        if processing_class is None:
            processing_class = AutoProcessor.from_pretrained(
                get_config_model_id(model.config),
                truncation_side="left",
                padding_side="left",
                trust_remote_code=args.trust_remote_code,
            )

        if args.use_transformers_continuous_batching and isinstance(processing_class, ProcessorMixin):
            raise ValueError(
                "`use_transformers_continuous_batching` does not support multimodal models. Use `use_vllm` instead."
            )

        # Handle pad token for processors or tokenizers
        if isinstance(processing_class, ProcessorMixin):
            self._tokenizer = processing_class.tokenizer
            self._is_vlm = True
        elif isinstance(processing_class, PreTrainedTokenizerBase):
            self._tokenizer = processing_class
            self._is_vlm = False
        else:
            raise TypeError("The `processing_class` must be either a `PreTrainedTokenizerBase` or a `ProcessorMixin`")

        if self._tokenizer.pad_token is None:
            self._tokenizer.pad_token = self._tokenizer.eos_token

        # Resolve vision placeholder token IDs once. Used by the forward pass to rebuild mm_token_type_ids
        # when tool responses inject images into the completion [see _generate forward_kwargs block].
        self._image_pad_token_id = None
        self._video_pad_token_id = None
        if self._is_vlm:
            for candidate in ("<|image_pad|>", "<|image|>"):
                tid = self._tokenizer.convert_tokens_to_ids(candidate)
                if tid != self._tokenizer.unk_token_id:
                    self._image_pad_token_id = tid
                    break
            tid = self._tokenizer.convert_tokens_to_ids("<|video_pad|>")
            if tid != self._tokenizer.unk_token_id:
                self._video_pad_token_id = tid

        # PEFT
        if False:
            if not is_peft_available():
                raise ImportError(
                    "You passed `peft_config` but the `peft` library is not installed. "
                    "Install it with `pip install trl[peft]`."
                )
            if not isinstance(peft_config, PeftConfig):
                raise TypeError(
                    f"`peft_config` must be a `peft.PeftConfig` instance (e.g. `peft.LoraConfig`), "
                    f"got {type(peft_config).__name__}."
                )
            if is_peft_model(model):
                raise ValueError(
                    "You passed a `PeftModel` instance together with a `peft_config` to the trainer. Please first merge "
                    "and unload the existing adapter, save the resulting base model, and then pass that base model along "
                    "with the new `peft_config` to the trainer."
                )
            # Create PEFT model
            # ZeRO-3 + PEFT for non-quantized models:
            # - PEFT's default autocast_adapter_dtype=True upcasts LoRA adapter params to fp32 even when the base model is bf16.
            # - ZeRO-3's _allgather_params_coalesced allocates output buffers using the dtype of the first persistent parameter,
            #   so mixed-dtype persistent_parameters [bf16 base + fp32 LoRA] cause a TypeError on the first optimizer step.
            # - Passing autocast_adapter_dtype=False keeps adapter params in the base model dtype [bf16], fixing the mismatch.
            # - This is safe: the fp32 upcast is a QLoRA-specific concern [low-bit quantized base models], not needed for
            #   non-quantized bf16 training.
            # - See:
            #   - TRL issue: https://github.com/huggingface/trl/issues/6089
            #   - Upstream issue: https://github.com/deepspeedai/DeepSpeed/issues/8072
            # - autocast_adapter_dtype was introduced in PEFT 0.12.0; before, no upcast existed: no need to pass the kwarg
            get_peft_model_kwargs = {}
            if (
                args.deepspeed_plugin is not None
                and args.deepspeed_plugin.zero_stage == 3
                and not _is_quantized_model
                and Version(peft.__version__) >= Version("0.12.0")
            ):
                get_peft_model_kwargs["autocast_adapter_dtype"] = False
            model = get_peft_model(model, peft_config, **get_peft_model_kwargs)

        elif is_peft_model(model) and args.beta != 0.0:
            # If the model is a PEFT model with a pretrained adapter, we need to create a "ref" adapter that is a copy
            # of the "default" adapter, so that we can use it as the reference model during GRPO training. PEFT only
            # supports one adapter per model when the LoRA config uses `target_parameters` [see peft#3340], so in that
            # case we skip the "ref" adapter and compute the reference log probs with adapters disabled, i.e. with the
            # base model.
            default_config = model.peft_config["default"]
            if isinstance(default_config, LoraConfig) and default_config.target_parameters:
                logger.warning(
                    "PEFT can't add a frozen reference adapter alongside one that uses `target_parameters` "
                    "(peft#3340], so the reference log probs are computed from the base model [adapters disabled]. "
                    "If you wrapped the model only to apply LoRA, pass a `peft_config` to the trainer instead; if you "
                    "wrapped it deliberately (pretrained adapter or custom init), note that the base model matches "
                    "your adapter only when it's freshly zero-initialized. If it is, this warning is safe to ignore."
                )
            else:
                model.add_adapter("ref", default_config)
                for name, param in model.named_parameters():
                    if ".default." in name:
                        ref_name = name.replace(".default.", ".ref.")
                        ref_param = model.get_parameter(ref_name)
                        ref_param.data.copy_(param.data)

        # When using gradient checkpointing with PEFT, we need to enable input gradients. transformers.Trainer normally
        # handles this, but a bug currently prevents it; see https://github.com/huggingface/transformers/issues/42489
        if is_peft_model(model) and args.gradient_checkpointing:
            model.enable_input_require_grads()

        # When using QLoRA, the PEFT adapter weights are converted to bf16 to follow the recommendations from the
        # original paper [see https://huggingface.co/papers/2305.14314, paragraph 3]. Normally, this can be done by
        # passing `autocast_adapter_dtype=False` to `get_peft_model`, but this option is not yet supported for
        # quantized models. See: https://github.com/huggingface/peft/issues/2889
        if _is_quantized_model:
            for param in model.parameters():
                if param.requires_grad:
                    param.data = param.data.to(torch.bfloat16)

        # Reward functions
        if not isinstance(reward_funcs, list):
            reward_funcs = [reward_funcs]
        self.reward_func_names = []
        for i, reward_func in enumerate(reward_funcs):
            if isinstance(reward_func, str):
                model_init_kwargs = args.model_init_kwargs or {}
                # Distributed training requires device_map=None ["auto" fails]
                if args.distributed_state.distributed_type in ["MULTI_GPU", "DEEPSPEED"]:
                    model_init_kwargs["device_map"] = None
                model_init_kwargs.setdefault("trust_remote_code", args.trust_remote_code)
                reward_funcs[i] = AutoModelForSequenceClassification.from_pretrained(
                    reward_func, num_labels=1, **model_init_kwargs
                )
            if isinstance(reward_funcs[i], nn.Module):  # Use Module over PretrainedModel for compat w/ compiled models
                self.reward_func_names.append(get_config_model_id(reward_funcs[i].config).split("/")[-1])
            else:
                self.reward_func_names.append(reward_funcs[i].__name__)
        self.reward_funcs = reward_funcs

        # Reward weights
        if args.reward_weights is not None:
            if len(args.reward_weights) != len(reward_funcs):
                raise ValueError(
                    f"Number of reward weights ({len(args.reward_weights)}) must match number of reward "
                    f"functions ({len(reward_funcs)})"
                )
            self.reward_weights = torch.tensor(args.reward_weights, dtype=torch.float32)
        else:
            self.reward_weights = torch.ones(len(reward_funcs), dtype=torch.float32)

        # Reward processing class
        if reward_processing_classes is None:
            reward_processing_classes = [None] * len(reward_funcs)
        elif not isinstance(reward_processing_classes, list):
            reward_processing_classes = [reward_processing_classes]
        if len(reward_processing_classes) != len(reward_funcs):
            raise ValueError(
                f"The number of reward processing classes ({len(reward_processing_classes)}) must match the number of "
                f"reward functions ({len(reward_funcs)})."
            )

        for i, (reward_processing_class, reward_func) in enumerate(
            zip(reward_processing_classes, reward_funcs, strict=True)
        ):
            if isinstance(reward_func, PreTrainedModel):
                if reward_processing_class is None:
                    reward_processing_class = AutoTokenizer.from_pretrained(
                        get_config_model_id(reward_func.config), trust_remote_code=args.trust_remote_code
                    )
                if reward_processing_class.pad_token_id is None:
                    reward_processing_class.pad_token = reward_processing_class.eos_token
                # The reward model computes the reward for the latest non-padded token in the input sequence.
                # So it's important to set the pad token ID to the padding token ID of the processing class.
                reward_func.config.pad_token_id = reward_processing_class.pad_token_id
                reward_processing_classes[i] = reward_processing_class

        self.reward_processing_classes = reward_processing_classes

        # Rollout function
        if rollout_func is not None and os.environ.get("TRL_EXPERIMENTAL_SILENCE", "0") != "1":
            warnings.warn(
                "You are using 'rollout_func', which is an experimental feature. This API may change or be removed at "
                "any time without prior notice. Silence this warning by setting environment variable "
                "TRL_EXPERIMENTAL_SILENCE=1.",
                UserWarning,
                stacklevel=2,
            )
        self.rollout_func = rollout_func
        if environment_factory is not None and os.environ.get("TRL_EXPERIMENTAL_SILENCE", "0") != "1":
            warnings.warn(
                "You are using 'environment_factory', which is an experimental feature. This API may change or be "
                "removed at any time without prior notice. Silence this warning by setting environment variable "
                "TRL_EXPERIMENTAL_SILENCE=1.",
                UserWarning,
                stacklevel=2,
            )

        # Tools
        if tools:
            if not Version(transformers.__version__) >= Version("5.0.0"):
                raise ImportError(
                    "Using tools with GRPOTrainer requires transformers version 5.0.0 or higher. Please upgrade "
                    "transformers with `pip install --upgrade transformers` to use this feature."
                )
        if environment_factory:
            if not Version(transformers.__version__) >= Version("5.2.0"):
                raise ImportError(
                    "Using `environment_factory` with GRPOTrainer requires transformers version 5.2.0 or higher. "
                    "Please install transformers from the main branch with `pip install "
                    "git+https://github.com/huggingface/transformers.git@main` to use this feature."
                )
        if tools or environment_factory:
            if not is_jmespath_available():
                raise ImportError(
                    "Using tools with GRPOTrainer requires the jmespath library for response parsing. Please install "
                    "it with `pip install jmespath` to use this feature."
                )
            if not supports_tool_calling(processing_class):
                raise ValueError(
                    "The provided chat template does not support tool calling. The template must be able to render a "
                    "full tool-calling conversation (user -> assistant with tool_calls -> tool)."
                )

        # Create the environments and extract their methods to be used as tools. We create one environment per rollout
        generation_batch_size = args.per_device_train_batch_size * args.steps_per_generation
        if environment_factory is not None:
            self.environments = [environment_factory() for _ in range(generation_batch_size)]
            environment_methods = [[] for _ in range(generation_batch_size)]
            for i, environment in enumerate(self.environments):
                has_reset = False
                for name, member in inspect.getmembers(environment, predicate=inspect.ismethod):
                    if name == "reset":
                        has_reset = True
                    elif not name.startswith("_"):
                        environment_methods[i].append(member)
                if not has_reset:
                    raise ValueError(
                        "Each environment instance returned by `environment_factory` must define a callable `reset` "
                    )
        else:
            self.environments = None

        tools = tools or []
        self._sync_tool_dicts = [{} for _ in range(generation_batch_size)]
        self._async_tool_dicts = [{} for _ in range(generation_batch_size)]
        for i in range(generation_batch_size):
            for tool in tools + (environment_methods[i] if self.environments is not None else []):
                if inspect.iscoroutinefunction(tool):
                    self._async_tool_dicts[i][tool.__name__] = tool
                else:
                    self._sync_tool_dicts[i][tool.__name__] = tool

        self.tools = tools + (environment_methods[0] if self.environments is not None else [])

        # Check for async functions to start an event loop on a daemon thread
        self._has_async_funcs = any(inspect.iscoroutinefunction(func) for func in self.reward_funcs + self.tools)

        if self._has_async_funcs:
            self.async_loop_thread, self.async_loop, self.async_loop_ready_event = start_event_loop_in_daemon(
                name="GRPOTrainer-AsyncLoop"
            )
            # wait until the event loop is running in the daemon thread
            self.async_loop_ready_event.wait()
            atexit.register(shutdown_event_loop_in_daemon, self.async_loop_thread, self.async_loop)

        # At the time of initial implementation, most tokenizers do not have built-in support for response schemas.
        # While waiting for broader adoption, we provide this utility function to manually set the response schema for
        # known chat templates. `response_schema` lives on the [inner] tokenizer, since `parse_response` is a tokenizer
        # method that reads `self.response_schema`.
        if self.tools and getattr(self._tokenizer, "response_schema", None) is None:
            processing_class = add_response_schema(processing_class)
        # In multi-turn training, the chat template *must* be prefix-preserving. If the tokenizer's original template
        # isn't, we replace it at initialization with a training-safe, prefix-preserving template.
        if self.tools and not is_chat_template_prefix_preserving(processing_class):
            self.chat_template = get_training_chat_template(processing_class)
        else:
            self.chat_template = None

        # Training arguments
        self.max_completion_length = args.max_completion_length  # = |o_i| in the GRPO paper
        self.num_generations = args.num_generations  # = G in the GRPO paper
        self.max_tool_calling_iterations = args.max_tool_calling_iterations or sys.maxsize
        self.num_generations_eval = args.num_generations_eval or self.num_generations
        self.chat_template_kwargs = args.chat_template_kwargs or {}
        self.temperature = args.temperature
        self.top_p = args.top_p
        self.top_k = args.top_k
        self.min_p = args.min_p
        self.repetition_penalty = args.repetition_penalty
        self.use_transformers_continuous_batching = args.use_transformers_continuous_batching
        if self.use_transformers_continuous_batching:
            if not Version(transformers.__version__) >= Version("5.8.0"):
                raise ImportError(
                    "Using `use_transformers_continuous_batching` requires transformers>=5.8.0. "
                    "Please upgrade with `pip install --upgrade transformers`."
                )
            from transformers.generation import ContinuousBatchingConfig

            cb_kwargs = dict(args.transformers_continuous_batching_config or {})
            # The transformers default [0.9] leaves almost no VRAM for the training backward pass;
            # use a training-aware default unless the user has set it explicitly.
            cb_kwargs.setdefault("max_memory_percent", 0.5)
            self.continuous_batching_config = ContinuousBatchingConfig(**cb_kwargs)
        else:
            self.continuous_batching_config = None
        self.pad_to_multiple_of = args.pad_to_multiple_of
        self.use_vllm = args.use_vllm
        self.vllm_mode = args.vllm_mode
        self.vllm_gpu_memory_utilization = args.vllm_gpu_memory_utilization  # only applies to colocation mode
        self.vllm_tensor_parallel_size = args.vllm_tensor_parallel_size  # only applies to colocation mode
        self.vllm_importance_sampling_correction = args.vllm_importance_sampling_correction
        self.vllm_importance_sampling_mode = args.vllm_importance_sampling_mode
        self.vllm_importance_sampling_clip_max = args.vllm_importance_sampling_clip_max
        self.vllm_importance_sampling_clip_min = args.vllm_importance_sampling_clip_min
        self.use_liger_kernel = args.use_liger_kernel
        self.loss_type = args.loss_type
        self.multi_objective_aggregation = args.multi_objective_aggregation

        # MoE load-balancing auxiliary loss, applied to Mixture-of-Experts models [no effect otherwise]
        text_config = model.config.get_text_config()
        is_moe = getattr(text_config, "output_router_logits", None) is not None
        self.aux_loss_enabled = is_moe and args.router_aux_loss_coef != 0.0
        self.router_aux_loss_coef = args.router_aux_loss_coef
        self.scale_rewards = args.scale_rewards
        self.importance_sampling_level = args.importance_sampling_level
        self.off_policy_mask_threshold = args.off_policy_mask_threshold
        if self.use_liger_kernel and self.off_policy_mask_threshold is not None:
            raise ValueError("Liger kernel does not support off-policy sequence masking yet.")
        if self.use_liger_kernel and is_peft_model(model):
            # The Liger fused GRPO loss multiplies the hidden states by `lm_head.weight` directly. When the LM head is
            # targeted by a PEFT adapter [`"lm_head"` in `target_modules`], `lm_head.weight` is the frozen base weight
            # and the trainable adapter parameters live in separate submodules that Liger never sees. The head adapter
            # would silently receive no gradient, so the model trains as if `lm_head` were frozen. Fail loudly rather
            # than train a silently-frozen head.
            output_embeddings = model.get_output_embeddings()
            if isinstance(output_embeddings, BaseTunerLayer):
                raise ValueError(
                    "`use_liger_kernel=True` is incompatible with applying a PEFT adapter to `lm_head`. The Liger "
                    "fused GRPO loss reads `lm_head.weight` directly, so the adapter on the head is ignored and never "
                    "trained. Either remove `'lm_head'` from your `target_modules`, or set `use_liger_kernel=False`."
                )
        self.mask_truncated_completions = args.mask_truncated_completions
        self.top_entropy_quantile = args.top_entropy_quantile
        if self.use_liger_kernel and self.top_entropy_quantile < 1.0:
            raise NotImplementedError(
                "Liger Kernels don't currently support masking token positions based on entropy."
            )
        if self.use_liger_kernel and self.importance_sampling_level not in ("token", "sequence"):
            raise ValueError(
                f"Unknown importance sampling level: {self.importance_sampling_level}. "
                "Possible values are 'token' and 'sequence'."
            )

        # Datasets
        self.shuffle_dataset = args.shuffle_dataset

        if train_dataset is None:
            raise ValueError("`train_dataset` is required")
        elif (
            isinstance(train_dataset, IterableDataset)
            or isinstance(eval_dataset, IterableDataset)
            or (
                isinstance(eval_dataset, dict) and any(isinstance(ds, IterableDataset) for ds in eval_dataset.values())
            )
        ):
            # See https://github.com/huggingface/trl/issues/3213
            raise NotImplementedError(
                "Iterable datasets are not yet supported in GRPOTrainer. Please use a standard dataset instead."
            )

        if args.loss_type == "luspo" and args.importance_sampling_level != "sequence":
            logger.warning(
                "When using `'luspo'` loss, `importance_sampling_level` should be set to `'sequence'` to mirror the "
                "paper's setup."
            )

        if args.loss_type == "vespo" and args.importance_sampling_level != "token":
            logger.warning(
                "VESPO computes sequence-level importance weights internally. `importance_sampling_level` should be "
                "set to `'token'` (the default)."
            )

        if args.importance_sampling_level == "sequence" and args.loss_type in ["bnpo", "dr_grpo", "dapo", "cispo"]:
            logger.warning(
                f"When using `importance_sampling_level='sequence'`, the `'{args.loss_type}'` loss sums per-token "
                "contributions, which effectively weights each sequence by its completion length instead of "
                "optimizing the per-sequence objective. To reproduce the GSPO paper's setup, set `loss_type='grpo'` "
                "(see https://huggingface.co/docs/trl/main/en/paper_index#group-sequence-policy-optimization]."
            )

        if self.loss_type == "vespo" and self.use_vllm and self.vllm_importance_sampling_correction:
            if self.vllm_importance_sampling_mode not in ["token_truncate", "token_mask"]:
                raise ValueError(
                    f"VESPO loss requires `vllm_importance_sampling_mode` to be either 'token_truncate' or "
                    f"'token_mask'. Got: {self.vllm_importance_sampling_mode}."
                )

        # Multi-step
        self.num_iterations = args.num_iterations  # = 𝜇 in the GRPO paper
        self.epsilon_low = args.epsilon
        self.epsilon_high = args.epsilon_high if args.epsilon_high is not None else args.epsilon
        # Tracks the number of iterations [forward + backward passes], including those within a grad accum cycle
        self._step = 0
        # Buffer the batch to reuse generated outputs across multiple updates. For more details, see
        # `_get_train_sampler` and `_prepare_inputs`.
        self._buffered_inputs = None

        # Transformers explicitly set use_reentrant=True in the past to silence a PyTorch warning, but the default was
        # never updated once PyTorch switched to recommending use_reentrant=False. Until that change lands upstream
        # [see https://github.com/huggingface/transformers/pull/43203] and is released [most likely in 5.0.0], we
        # default to the recommended non-reentrant behavior here, while preserving any user-provided value.
        if args.gradient_checkpointing and Version(transformers.__version__) < Version("5.0.0"):
            args.gradient_checkpointing_kwargs = args.gradient_checkpointing_kwargs or {}
            args.gradient_checkpointing_kwargs.setdefault("use_reentrant", False)

        super().__init__(
            model=model,
            args=args,
            data_collator=identity,  # No data collation is needed in GRPO
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            processing_class=processing_class,
            callbacks=callbacks,
            optimizers=optimizers,
            # In Trainer, `training_step` scales the loss by `gradient_accumulation_steps` only if `compute_loss_func`
            # is None. For DAPO, loss scaling instead depends on the total number of completions tokens across the
            # global accumulated batch. To control scaling ourselves, we must disable Trainer’s built-in scaling. The
            # simplest [though a bit hacky] way is to set `compute_loss_func` to any non-None value, which bypasses
            # that behavior without rewriting `training_step`.
            compute_loss_func="non-None value to disable scaling",
        )

        # Reference model
        self.beta = args.beta
        if self.beta == 0.0:
            # If beta is 0.0, the reference model is not needed
            self.ref_model = None
        elif is_peft_model(model):
            # If PEFT is used, the reference model is not needed since the adapter can be disabled
            # to revert to the initial model.
            self.ref_model = None
        else:
            # For deepspeed, fsdp or non-distributed models, create a reference model from scratch
            model_init_kwargs = args.model_init_kwargs or {}
            # Distributed training requires device_map=None ["auto" fails]
            if self.args.distributed_state.distributed_type in ["MULTI_GPU", "DEEPSPEED"]:
                model_init_kwargs["device_map"] = None
            model_init_kwargs.setdefault("trust_remote_code", args.trust_remote_code)
            self.ref_model = create_model_from_path(get_config_model_id(self.model.config), **model_init_kwargs)

        # Disable dropout in the models
        if args.disable_dropout:
            disable_dropout_in_model(model)
            if self.ref_model is not None:
                disable_dropout_in_model(self.ref_model)

        # Cast LM Head To FP32
        if args.cast_lm_head_to_fp32:

            def _cast_lm_head_to_fp32(target_model: PreTrainedModel):
                """Cast lm_head to fp32 while preserving embedding output dtype if tied."""

                def cast_inputs_to_fp32(module, inputs):
                    # Preserve other positional args and kwargs untouched
                    if not inputs:
                        return inputs
                    return (inputs[0].to(torch.float32),) + inputs[1:]

                original_dtype_local = target_model.lm_head.weight.dtype
                target_model.lm_head = target_model.lm_head.float()
                target_model.lm_head.register_forward_pre_hook(cast_inputs_to_fp32)

                if target_model.config.tie_word_embeddings:

                    def cast_outputs_to_original_dtype(module, args, output):
                        return output.to(original_dtype_local)

                    # Only cast activations; weights are now fp32 [intentional for numerical stability of logits]
                    target_model.model.embed_tokens.register_forward_hook(cast_outputs_to_original_dtype)

            _cast_lm_head_to_fp32(model)
            if self.ref_model is not None:
                _cast_lm_head_to_fp32(self.ref_model)

        # Liger loss
        if self.use_liger_kernel:
            if not is_liger_kernel_available():
                raise ImportError(
                    "Liger is required to use `use_liger_kernel` as the GRPO loss. Run `pip install liger-kernel`."
                )
            # redirect the model.module forward to the model forward to ensure pre-forward hooks are called
            self._forward_redirection = _ForwardRedirection()

            self.liger_grpo_loss = LigerFusedLinearGRPOLoss(
                beta=self.beta,
                epsilon_low=self.epsilon_low,
                epsilon_high=self.epsilon_high,
                temperature=self.temperature,
                use_ref_model=self.beta != 0.0,
                loss_type=self.loss_type,
                max_completion_length=self.max_completion_length,
                importance_sampling_level=self.importance_sampling_level,
                delta=args.delta,
                use_bias_correction_kl=args.use_bias_correction_kl,
                sapo_temperature_pos=args.sapo_temperature_pos,
                sapo_temperature_neg=args.sapo_temperature_neg,
                vespo_k_pos=args.vespo_k_pos,
                vespo_lambda_pos=args.vespo_lambda_pos,
                vespo_k_neg=args.vespo_k_neg,
                vespo_lambda_neg=args.vespo_lambda_neg,
            )

        # Initialize the metrics
        self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)}
        self._total_train_tokens = 0
        self._current_train_step_time = 0.0
        self.log_completions = args.log_completions
        self.log_unique_prompts = args.log_unique_prompts
        self.num_completions_to_print = args.num_completions_to_print
        # Keep logs sized to the generation batch to record only outputs from the latest model update.
        self._logs = {
            "images": deque(maxlen=args.generation_batch_size),
            "prompt": deque(maxlen=args.generation_batch_size),
            "completion": deque(maxlen=args.generation_batch_size),
            "rewards": defaultdict(lambda: deque(maxlen=args.generation_batch_size)),
            "advantages": deque(maxlen=args.generation_batch_size),
            "extra": defaultdict(lambda: deque(maxlen=args.generation_batch_size)),
        }
        # Buffers for user-logged data from reward functions, flushed after gathering
        self._pending_extra_logs = defaultdict(list)
        self._pending_metrics = defaultdict(list)

        # Ensure each process receives a unique seed to prevent duplicate completions when generating with
        # transformers if num_generations exceeds per_device_train_batch_size. We could skip it if we use vLLM, but
        # it's safer to set it in all cases.
        set_seed(args.seed, device_specific=True)

        if self.use_vllm:
            self.vllm_generation = VLLMGeneration(
                model=self.model,
                accelerator=self.accelerator,
                processing_class=self.processing_class,
                mode=args.vllm_mode,
                structured_outputs_regex=args.vllm_structured_outputs_regex,
                server_base_url=args.vllm_server_base_url,
                server_host=args.vllm_server_host,
                server_port=args.vllm_server_port,
                group_port=args.vllm_group_port,
                server_timeout=args.vllm_server_timeout,
                tensor_parallel_size=args.vllm_tensor_parallel_size,
                gpu_memory_utilization=args.vllm_gpu_memory_utilization,
                max_model_length=args.vllm_max_model_length,
                max_num_seqs=args.per_device_train_batch_size
                * args.vllm_tensor_parallel_size
                * args.steps_per_generation,
                enable_sleep_mode=args.vllm_enable_sleep_mode,
                model_impl=args.vllm_model_impl,
                repetition_penalty=self.repetition_penalty,
                temperature=self.temperature,
                top_p=self.top_p,
                top_k=self.top_k,
                min_p=self.min_p,
                max_completion_length=self.max_completion_length,
                logprobs=0,
                generation_kwargs=args.generation_kwargs,
            )
            self._last_loaded_step = -1
        else:
            generation_kwargs = {
                "max_new_tokens": self.max_completion_length,
                "do_sample": True,
                "pad_token_id": self._tokenizer.pad_token_id,
                "bos_token_id": self._tokenizer.bos_token_id,
                "eos_token_id": self._tokenizer.eos_token_id,
                "temperature": self.temperature,
                "top_p": self.top_p,
                "top_k": self.top_k,
                "min_p": self.min_p,
                "repetition_penalty": self.repetition_penalty,
                "cache_implementation": args.cache_implementation,
            }
            if args.generation_kwargs is not None:
                generation_kwargs.update(args.generation_kwargs)
            self.generation_config = GenerationConfig(**generation_kwargs, disable_compile=True)
            # Keep training-specific generation kwargs to overwrite model's original generation config
            self.generation_kwargs = generation_kwargs

        # Gradient accumulation requires scaled loss. Normally, loss scaling in the parent class depends on whether the
        # model accepts loss-related kwargs. Since we compute our own loss, this check is irrelevant. We set
        # self.model_accepts_loss_kwargs to False to enable scaling.
        self.model_accepts_loss_kwargs = False
        self._dist = DistributedBackend(self.accelerator)

        # Add tags to the model
        self.model.add_model_tags(self._tag_names)

        if self.ref_model is not None:
            if self.is_deepspeed_enabled:
                self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator)
            elif self.is_fsdp_enabled:
                self.ref_model = prepare_fsdp(self.ref_model, self.accelerator)
            else:
                self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)

        if args.sync_ref_model:
            if self.beta == 0.0:
                raise ValueError(
                    "You passed `sync_ref_model=True` while `beta=0.0`, which means the reference model is not used "
                    "during training. Consequently, GRPOTrainer does not create a `ref_model` instance, and there is "
                    "nothing to synchronize. Please set `sync_ref_model=False`, or set `beta` to a non-zero value."
                )
            if is_peft_model(model):
                raise NotImplementedError(
                    "You passed `sync_ref_model=True` while using a PEFT model, which is currently not supported. "
                    "With PEFT, GRPOTrainer does not keep a separate reference model in memory; instead, it recovers "
                    "reference behavior by temporarily disabling the adapter. As a result, there is no standalone "
                    "`ref_model` instance to synchronize. Use `sync_ref_model=False`, or opt for full fine-tuning if "
                    "you need a synced reference model. If you need `sync_ref_model` to work with PEFT, please open a "
                    "feature request at https://github.com/huggingface/trl/issues."
                )
            self.add_callback(SyncRefModelCallback(ref_model=self.ref_model, accelerator=self.accelerator))

        for i, reward_func in enumerate(self.reward_funcs):
            if isinstance(reward_func, PreTrainedModel):
                if self.is_deepspeed_enabled:
                    self.reward_funcs[i] = prepare_deepspeed(reward_func, self.accelerator)
                else:
                    # set device placement to True to make `prepare_model` move `reward_func` to device when using fsdp
                    self.reward_funcs[i] = self.accelerator.prepare_model(
                        reward_func, evaluation_mode=True, device_placement=True
                    )

        if self.accelerator.is_main_process and self.log_completions:
            os.makedirs(os.path.join(self.args.output_dir, "completions"), exist_ok=True)
            if self.args.log_completions_hub_repo is not None:
                repo_id = self.args.log_completions_hub_repo
                create_repo(repo_id, private=self.args.hub_private_repo, repo_type="dataset", exist_ok=True)
                template_path = pkg_resources.files("trl").joinpath("templates/completions_dataset_card.md")
                card_data = DatasetCardData(
                    pretty_name="TRL Completion logs",
                    tags=["trl", "trl-logs", "completions"],
                )
                card = DatasetCard.from_template(
                    card_data=card_data,
                    template_path=str(template_path),
                    repo_id=repo_id,
                    hub_model_id=self.args.hub_model_id,
                )
                card.push_to_hub(repo_id)
                self.commit_scheduler = CommitScheduler(
                    repo_id=repo_id,
                    repo_type="dataset",
                    folder_path=f"{self.args.output_dir}/completions",
                    every=2,  # minutes
                    allow_patterns=["*.parquet"],
                )

    def _set_signature_columns_if_needed(self):
        # If `self.args.remove_unused_columns` is True, non-signature columns are removed.
        # By default, this method sets `self._signature_columns` to the model's expected inputs (usually, "input_ids"
        # and "attention_mask"). In GRPOTrainer, we preprocess data, so using the model's signature columns doesn't
        # work. Instead, we set them to the columns expected by the `training_step` method, hence the override.
        if self._signature_columns is None:
            self._signature_columns = ["prompt", "image", "images"]

    # This method overrides `Trainer.get_train_dataloader` to support our custom batching strategy.
    # Instead of returning a standard per-step batch (i.e., `per_device_batch_size), our dataloader loads an
    # *generation* batch (i.e., `per_device_batch_size × steps_per_generation`). This allows us to generate completions
    # once every steps_per_generation step—rather than once per accumulation step—which is significantly more
    # efficient. The only change from the original implementation is multiplying the batch size by
    # `steps_per_generation`. Thus, `_prepare_inputs` is called with this *generation* batch, and it handles the
    # splitting internally.
    # Maintenance note: This method is a copy-paste of the original `Trainer.get_train_dataloader` with only one line
    # modification.
    def get_train_dataloader(self):
        return self._get_dataloader(
            dataset=self.train_dataset,
            description="Training",
            batch_size=self._train_batch_size * self.args.steps_per_generation,  # < this is the change
            sampler_fn=self._get_train_sampler,
            is_training=True,
        )

    def _get_train_sampler(self, dataset: Dataset | None = None) -> Sampler:
        # Returns a sampler that
        # 1. ensures each prompt is repeated across multiple processes. This guarantees that identical prompts are
        #    distributed to different GPUs, allowing rewards to be computed and normalized correctly within each prompt
        #    group. Using the same seed across processes ensures consistent prompt assignment, preventing discrepancies
        #    in group formation.
        # 2. repeats the batch multiple times to allow reusing generations across multiple updates. Refer to
        #    _prepare_inputs to see how the generations are stored and reused.

        # In the following figure, the values are the prompt indices. Each row shows the per-step batch
        # returned by `_prepare_inputs`; rows within a `steps_per_generation` block are slices of the same
        # generated batch. When `num_iterations > 1`, that block is reused for multiple optimization passes
        # before regenerating.
        #
        #                                      |   GPU 0  |   GPU 1  |
        #
        #                 global_step   step    <-───>  num_generations=2
        #                                       <-───────> per_device_train_batch_size=3
        #  grad_accum    ▲  ▲  0          0     0   0   1   1   2   2   <- Generate for the first `steps_per_generation` (prompts 0 to 11); store the completions; use the first slice to compute the loss
        #     =2         ▼  |  0          1     3   3   4   4   5   5   <- Take the stored generations and use the second slice to compute the loss
        #                   |
        #                   |  1          2     6   6   7   7   8   8   <- Take the stored generations and use the third slice to compute the loss
        #  steps_per_gen=4  ▼  1          3     9   9  10  10  11  11   <- Take the stored generations and use the fourth slice to compute the loss
        #
        #                      2          4    12  12  13  13  14  14   <- Generate for the second `steps_per_generation` (prompts 12 to 23); store the completions; use the first slice to compute the loss
        #                      2          5    15  15  16  16  17  17   <- Take the stored generations and use the second slice to compute the loss
        #                                          ...
        if dataset is None:
            dataset = self.train_dataset
        return RepeatSampler(
            data_source=dataset,
            mini_repeat_count=self.num_generations,
            batch_size=self.args.generation_batch_size // self.num_generations,
            repeat_count=self.num_iterations * self.args.steps_per_generation,
            shuffle=self.shuffle_dataset,
            seed=self.args.seed,
        )

    def _get_eval_sampler(self, eval_dataset) -> Sampler:
        # See _get_train_sampler for an explanation of the sampler.
        return RepeatSampler(
            data_source=eval_dataset,
            mini_repeat_count=self.num_generations_eval,
            seed=self.args.seed,
        )

    @profiling_decorator
    def _get_last_hidden_state(
        self,
        unwrapped_model,
        input_ids,
        attention_mask,
        logits_to_keep,
        pixel_values=None,
        image_grid_thw=None,
        pixel_attention_mask=None,
        spatial_shapes=None,
        image_sizes=None,
        image_position_ids=None,
    ):
        if is_peft_model(unwrapped_model):
            unwrapped_model = unwrapped_model.base_model.model

        # Build model inputs - check if the model supports logits_to_keep (some models and VLMs don't)
        model_inputs = {"input_ids": input_ids, "attention_mask": attention_mask}

        # For Qwen models:
        if image_grid_thw is not None and pixel_values is not None:
            model_inputs["image_grid_thw"] = image_grid_thw
        # For Gemma, SmolVLM2, LLaVa-Next etc.:
        if pixel_values is not None:
            model_inputs["pixel_values"] = pixel_values
        # For SmolVLM2
        if pixel_attention_mask is not None:
            model_inputs["pixel_attention_mask"] = pixel_attention_mask
        # For LFM2-VL
        if spatial_shapes is not None:
            model_inputs["spatial_shapes"] = spatial_shapes
        # For LLaVa-Next
        if image_sizes is not None:
            model_inputs["image_sizes"] = image_sizes
        if image_position_ids is not None:
            model_inputs["image_position_ids"] = image_position_ids

        # Only add logits_to_keep if the model supports it
        if "logits_to_keep" in self.model_kwarg_keys:
            # We add 1 to `logits_to_keep` because the last logits of the sequence is later excluded
            model_inputs["logits_to_keep"] = logits_to_keep + 1

        model_inputs["use_cache"] = False  # only used in generation; set False to suppress warnings

        # `base_model` gives the backbone model (skipping `lm_head`) — text decoder for LMs, multimodal wrapper for
        # VLMs (so vision-token injection runs before the text decoder). `get_decoder()` won't do: on VLMs it
        # returns just the text stack and feeds image-placeholder IDs through it.
        # Pre-5.0 transformers VLMs set `base_model_prefix = ""` so `base_model is self` (re-runs `lm_head`).
        # Fall back to `.model` there.
        if self._is_vlm and Version(transformers.__version__) < Version("5.0.0"):
            backbone = unwrapped_model.model
        else:
            backbone = unwrapped_model.base_model
        last_hidden_state = backbone(**model_inputs).last_hidden_state
        # Exclude the last value: it corresponds to the next token pred
        last_hidden_state = last_hidden_state[:, :-1, :]  # (B, L-1, H)
        # Only keep the last logits_to_keep. For model that support logits_to_keep, this is a no-op.
        last_hidden_state = last_hidden_state[:, -logits_to_keep:, :]  # (B, logits_to_keep, H)
        return last_hidden_state

    def get_high_entropy_mask(self, entropies: torch.Tensor, mask: torch.Tensor, threshold: float) -> torch.Tensor:
        """
        Returns a binary mask identifying tokens whose entropy exceeds a given quantile threshold.

        Args:
            entropies (`torch.Tensor`):
                Tensor of shape (batch_size, seq_len) with per-token entropy values.
            mask (`torch.Tensor`):
                Binary mask of the same shape as `entropies`, where `1` indicates valid tokens and `0` padding.
            threshold (`float`):
                Quantile threshold between `0.0` and `1.0` to select high-entropy tokens.

        Returns:
            `torch.Tensor`:
                Boolean mask of shape (batch_size, seq_len), where `True` indicates tokens with entropy >= threshold
                and `False` otherwise.
        """
        local = entropies[mask.bool()].float()

        # Use a negative pad_value as a sentinel because entropy values are always >= 0.
        # This guarantees that the sentinel cannot collide with any real entropy value.
        pad_value = -1e9

        # Pad across processes so that every rank has the same tensor length
        padded = self.accelerator.pad_across_processes(local, dim=0, pad_index=pad_value)
        gathered = self.accelerator.gather(padded)

        # Drop sentinel values (safe because no entropy can be negative)
        gathered = gathered[gathered != pad_value]

        if gathered.numel() == 0:
            return torch.zeros_like(entropies, dtype=torch.bool)

        entropy_threshold = torch.quantile(gathered, threshold)
        masked_entropies = entropies * mask.float()
        entropy_mask = masked_entropies >= entropy_threshold
        return entropy_mask & mask.bool()  # ensure padding tokens are always masked out

    def _get_per_token_logps_and_entropies(
        self,
        model,
        input_ids,
        attention_mask,
        logits_to_keep,
        batch_size = None,
        compute_entropy = False,
        compute_efficient = False,
        *args,
        **kwargs,
    ):
        # All Unsloth code here in this function is licensed under AGPL3
        # if True: # os.environ.get('UNSLOTH_USE_NEW_MODEL', '0') == '0':
        #     return None, None  # logps, entropies Unsloth efficient GRPO
        if compute_efficient:
            return None, None
        else:
            if not hasattr(self, "_autocast_dtype"):
                self._autocast_dtype = (
                    torch.float16
                    if os.environ.get("ACCELERATE_MIXED_PRECISION", "fp16") == "fp16"
                    else torch.bfloat16
                )
                if os.environ.get("UNSLOTH_FORCE_FLOAT32", "0") == "1":
                    self._autocast_dtype = torch.float16

            pixel_values, image_grid_thw = (
                kwargs.get("pixel_values", None),
                kwargs.get("image_grid_thw", None),
            )
            pixel_attention_mask, image_sizes = (
                kwargs.get("pixel_attention_mask", None),
                kwargs.get("image_sizes", None),
            )
            num_images = kwargs.get("num_images", None)
            # Transformers 5.x needs token_type_ids/mm_token_type_ids for some vision models
            token_type_ids = kwargs.get("token_type_ids", None)
            mm_token_type_ids = kwargs.get("mm_token_type_ids", None)
            if mm_token_type_ids is not None or image_grid_thw is not None:
                mm_token_type_ids = _unsloth_fix_mm_token_type_ids(
                    self.processing_class, input_ids, mm_token_type_ids
                )

            unwrapped_model = self.accelerator.unwrap_model(model, keep_fp32_wrapper = False)

            lm_head = self.model.get_output_embeddings().weight

            dtype_bytes = 16 if self._autocast_dtype in [torch.float16, torch.bfloat16] else 32
            total_rows = input_ids.shape[0]
            seq_len = input_ids.shape[1]
            hidden_dim = lm_head.shape[1]
            vocab_dim = lm_head.shape[0]

            if self.args.unsloth_grpo_mini_batch is None:
                B, multiplier = autotune_batch_and_chunks(
                    total_rows,
                    seq_len,
                    hidden_dim,
                    vocab_dim,
                    dtype_bytes,
                    self.args.unsloth_logit_chunk_multiplier,
                )
                B = total_rows // B
            else:
                B = self.args.unsloth_grpo_mini_batch

                if self.args.unsloth_logit_chunk_multiplier is None:
                    multiplier = max(4, seq_len // 4096)
                else:
                    multiplier = self.args.unsloth_logit_chunk_multiplier

            all_logprobs_list = []
            if pixel_values is None:
                left_pad_tokens_per_prompt = calculate_pad_tokens_in_prompt(
                    input_ids, logits_to_keep, self.processing_class.pad_token_id
                )
                max_left_pad = torch.max(left_pad_tokens_per_prompt).item()
                input_ids = left_pack_padding(input_ids, self.processing_class.pad_token_id)
                attention_mask = input_ids != self.processing_class.pad_token_id
                attention_mask = attention_mask.to(attention_mask.dtype)
            else:
                max_left_pad = 0

            def slice_sample_axis(value, start, end):
                if value is None:
                    return None
                return value[start:end]

            import math

            total_samples = input_ids.shape[0]
            batch_size = math.ceil(total_samples / B)
            if isinstance(num_images, torch.Tensor):
                num_images = num_images.detach().cpu().reshape(-1).tolist()
            if image_grid_thw is not None and pixel_values is not None and num_images is not None:
                rows_per_image = image_grid_thw.prod(dim = -1)
                rows_per_sample = torch.split(rows_per_image, num_images)
                rows_per_sample = torch.stack([s.sum() for s in rows_per_sample])
                # why: cum_rows is indexed via .item() inside the per-chunk loop;
                # keeping it on CPU avoids per-iteration GPU->CPU sync.
                cum_rows = torch.cat(
                    [
                        torch.tensor([0], device = rows_per_sample.device),
                        rows_per_sample.cumsum(0),
                    ]
                ).cpu()
                cum_imgs = torch.tensor([0] + num_images).cumsum(0)
            else:
                cum_rows = None
                cum_imgs = None

            def _first_dim_len(value):
                if value is None:
                    return None
                if hasattr(value, "shape"):
                    return value.shape[0]
                try:
                    return len(value)
                except TypeError:
                    return None

            total_images = sum(num_images) if num_images is not None else None
            _image_sizes_n = _first_dim_len(image_sizes)

            input_ids_chunks = []
            attention_mask_chunks = []
            pixel_values_chunks = []
            image_grid_thw_chunks = []
            pixel_attention_mask_chunks = []
            image_sizes_chunks = []
            token_type_ids_chunks = []
            mm_token_type_ids_chunks = []

            current_pixel_idx = 0
            # TRL 0.23.0 batching logic
            for start in range(0, total_samples, batch_size):
                end = min(start + batch_size, total_samples)

                input_ids_chunks.append(input_ids[start:end])
                attention_mask_chunks.append(attention_mask[start:end])
                token_type_ids_chunks.append(slice_sample_axis(token_type_ids, start, end))
                mm_token_type_ids_chunks.append(slice_sample_axis(mm_token_type_ids, start, end))

                if image_grid_thw is not None and pixel_values is not None:
                    if num_images is None:
                        grid_slice = image_grid_thw[start:end]
                        batch_pixel_count = grid_slice.prod(dim = -1).sum().item()
                        start_pixel_idx = current_pixel_idx
                        end_pixel_idx = current_pixel_idx + batch_pixel_count
                        current_pixel_idx = end_pixel_idx
                        img_start = img_end = None
                    else:
                        start_pixel_idx = cum_rows[start].item()
                        end_pixel_idx = cum_rows[end].item()
                        img_start = cum_imgs[start].item()
                        img_end = cum_imgs[end].item()
                        grid_slice = image_grid_thw[img_start:img_end]
                    image_grid_thw_chunks.append(grid_slice)

                    pixel_values_chunks.append(pixel_values[start_pixel_idx:end_pixel_idx])

                    if image_sizes is None:
                        image_sizes_chunks.append(None)
                    elif (
                        num_images is not None
                        and _image_sizes_n == total_images
                        and img_start is not None
                    ):
                        image_sizes_chunks.append(image_sizes[img_start:img_end])
                    else:
                        image_sizes_chunks.append(slice_sample_axis(image_sizes, start, end))

                    if pixel_attention_mask is None:
                        pixel_attention_mask_chunks.append(None)
                    elif (
                        num_images is not None
                        and img_start is not None
                        and pixel_attention_mask.shape[0] == image_grid_thw.shape[0]
                    ):
                        pixel_attention_mask_chunks.append(pixel_attention_mask[img_start:img_end])
                    elif (
                        pixel_attention_mask.shape[0] == pixel_values.shape[0]
                        and pixel_attention_mask.shape[0] != input_ids.shape[0]
                    ):
                        pixel_attention_mask_chunks.append(
                            pixel_attention_mask[start_pixel_idx:end_pixel_idx]
                        )
                    else:
                        pixel_attention_mask_chunks.append(pixel_attention_mask[start:end])

                else:
                    pixel_values_chunks.append(None)
                    image_grid_thw_chunks.append(None)
                    pixel_attention_mask_chunks.append(None)
                    image_sizes_chunks.append(slice_sample_axis(image_sizes, start, end))

            temperature = self.temperature
            logit_softcapping = _unsloth_get_final_logit_softcapping(model.config)
            logit_scale_multiply = getattr(model.config, "logit_scale", 0)
            if logit_scale_multiply is None:
                logit_scale_multiply = 0
            logit_scale_divide = getattr(model.config, "logits_scaling", 0)
            if logit_scale_divide is None:
                logit_scale_divide = 0

            zipped_inputs = zip(
                input_ids_chunks,
                attention_mask_chunks,
                pixel_values_chunks,
                image_grid_thw_chunks,
                pixel_attention_mask_chunks,
                image_sizes_chunks,
                token_type_ids_chunks,
                mm_token_type_ids_chunks,
            )
            os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1"

            with _get_inference_mode_context_manager(model):
                for (
                    input_ids_chunk,
                    attention_mask_chunk,
                    pixel_values_chunk,
                    image_grid_thw_chunk,
                    pixel_attention_mask_chunk,
                    image_sizes_chunk,
                    token_type_ids_chunk,
                    mm_token_type_ids_chunk,
                ) in zipped_inputs:
                    _extra_vision_kwargs = {}
                    if token_type_ids_chunk is not None:
                        _extra_vision_kwargs["token_type_ids"] = token_type_ids_chunk
                    if mm_token_type_ids_chunk is not None:
                        _extra_vision_kwargs["mm_token_type_ids"] = mm_token_type_ids_chunk
                    with torch.amp.autocast(device_type = "cuda", dtype = self._autocast_dtype):
                        if pixel_values is None:
                            logits_chunk = unwrapped_model(
                                input_ids = input_ids_chunk,
                                attention_mask = attention_mask_chunk,
                                pixel_values = pixel_values_chunk,
                                image_grid_thw = image_grid_thw_chunk,
                                pixel_attention_mask = pixel_attention_mask_chunk,
                                image_sizes = image_sizes_chunk,
                                **_extra_vision_kwargs,
                            ).logits

                            completion_input_ids_chunk = input_ids_chunk[
                                :, -(logits_to_keep + max_left_pad) :
                            ]
                            logits_chunk = logits_chunk[
                                :, -(logits_to_keep + max_left_pad + 1) :, :
                            ]
                            logits_chunk = logits_chunk[:, :-1, :]
                            logprobs_chunk = chunked_hidden_states_selective_log_softmax(
                                logits_chunk,
                                lm_head,
                                completion_input_ids_chunk,
                                chunks = input_ids_chunk.shape[0] * multiplier,
                                logit_scale_multiply = logit_scale_multiply,
                                logit_scale_divide = logit_scale_divide,
                                logit_softcapping = logit_softcapping,
                                temperature = temperature,
                            )
                        else:
                            # Essentially, for VLMs we do not go via the optimized path in models/,
                            # so we don't encounter the Flash Attn left-padding issue.
                            logits_chunk = unwrapped_model(
                                input_ids = input_ids_chunk,
                                attention_mask = attention_mask_chunk,
                                pixel_values = pixel_values_chunk,
                                image_grid_thw = image_grid_thw_chunk,
                                pixel_attention_mask = pixel_attention_mask_chunk,
                                image_sizes = image_sizes_chunk,
                                logits_to_keep = logits_to_keep + 1,
                                **_extra_vision_kwargs,
                            ).logits

                            logits_chunk = logits_chunk[:, :-1, :]
                            completion_input_ids_chunk = input_ids_chunk[:, -logits_to_keep:]
                            # Guard: check if model returned hidden states or logits
                            if logits_chunk.shape[-1] == lm_head.shape[1]:
                                logprobs_chunk = chunked_hidden_states_selective_log_softmax(
                                    logits_chunk,
                                    lm_head,
                                    completion_input_ids_chunk,
                                    chunks = input_ids_chunk.shape[0] * multiplier,
                                    logit_scale_multiply = logit_scale_multiply,
                                    logit_scale_divide = logit_scale_divide,
                                    logit_softcapping = logit_softcapping,
                                    temperature = temperature,
                                )
                            else:
                                # Model returned logits directly - scaling/softcapping already applied by model forward
                                logprobs_chunk = chunked_selective_log_softmax(
                                    logits_chunk,
                                    completion_input_ids_chunk,
                                    temperature,
                                )
                    # This is needed to avoid race conditions with GPT OSS offload_embbed=True
                    # However, it seems that this line does not slow down or disrupt models.
                    device_synchronize()
                    all_logprobs_list.append(logprobs_chunk)
                logprobs = torch.cat(all_logprobs_list, dim = 0)
                entropies = None

            os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "0"

            return logprobs.detach(), entropies  # logps, entropies
            # input_ids = input_ids[:, -logits_to_keep:]
            # For transformers<=4.48, logits_to_keep argument isn't supported, so here we drop logits ourselves.
            # See https://github.com/huggingface/trl/issues/2770
            # logits = logits[:, -logits_to_keep:]
            # return logits
            # See https://huggingface.co/blog/the_n_implementation_details_of_rlhf_with_ppo#policy-training-implementation-details
            # logits = logits / self.temperature
            # logps = selective_log_softmax(logits, input_ids)

            # row_indices, col_indices = torch.where(logps < -20)

            # # Method 1: Check if tensors have elements
            # if len(row_indices) > 0 and len(col_indices) > 0:
            #     breakpoint()  # Breakpoint triggered here
            #     print("Found high values!")
            # return  logps #  compute logprobs for the input tokens

    def training_step(self, model, inputs, num_items_in_batch):
        time_before = time.perf_counter()
        output = super().training_step(model, inputs, num_items_in_batch)
        self._step += 1
        time_after = time.perf_counter()
        self._current_train_step_time += time_after - time_before
        if self._step % self.current_gradient_accumulation_steps == 0:
            self._metrics["train"]["step_time"].append(self._current_train_step_time)
            self._current_train_step_time = 0.0
        return output

    @profiling_decorator
    def _prepare_inputs(self, generation_batch: dict[str, torch.Tensor | Any]) -> dict[str, torch.Tensor | Any]:
        # Prepares inputs for model training/evaluation by managing completion generation and batch handling.
        # During training:
        #   - Receives the local generation batch (Per-GPU batch size × steps per generation)
        #     from the modified training dataloader instead of the standard local batch
        #   - Generates completions once for the entire generation batch and splits it into batches of size
        #     `per_device_train_batch_size`
        #   - Buffers these completions and returns the appropriate slice for the current accumulation step
        #   - Optimizes by regenerating completions only periodically (every steps_per_generation * num_iterations)
        # During evaluation:
        #   - The input is treated as a standard local batch (no accumulation, no multiple iterations)
        #   - Completions are generated for each batch without buffering or reuse
        # Returns a single local batch in both cases.

        mode = "train" if self.model.training else "eval"
        if mode == "train":
            generate_every = self.args.steps_per_generation * self.num_iterations
            if self._step % generate_every == 0 or self._buffered_inputs is None:
                # self._buffered_inputs=None can occur when resuming from a checkpoint
                generation_batch = self._generate_and_score_completions(generation_batch)
                generation_batch = split_pixel_values_by_grid(generation_batch)

                try: generation_batch = shuffle_sequence_dict(generation_batch)

                except: pass
                generation_batches = split_tensor_dict(generation_batch, self.args.steps_per_generation)
                self._buffered_inputs = [unsplit_pixel_values_by_grid(batch) for batch in generation_batches]
            inputs = self._buffered_inputs[self._step % self.args.steps_per_generation]
        else:
            # In evaluation, there is neither batch grouping for generation, nor multiple iterations, hence
            # local generation batch == local eval batch
            inputs = self._generate_and_score_completions(generation_batch)
        return inputs

    def _log_completion_extra(self, column: str, values: list):
        """
        Log extra columns to the completions table. Called from reward functions via the `log_extra` kwarg.

        Args:
            column (`str`):
                Name of the column to add.
            values (`list`):
                Values for the column, one per sample in the batch.
        """
        self._pending_extra_logs[column].extend(values)

    def _log_metric(self, name: str, value: float):
        """
        Log a scalar metric from a reward function. Called via the `log_metric` kwarg. Values are averaged over each
        logging step and reported alongside built-in metrics like `kl` and `entropy`.

        Args:
            name (`str`):
                Name of the metric.
            value (`float`):
                Scalar value for this batch.
        """
        self._pending_metrics[name].append(value)

    @profiling_decorator
    def _calculate_rewards(self, inputs, prompts, completions, completion_ids_list):
        device = self.accelerator.device
        rewards_per_func = torch.zeros(len(prompts), len(self.reward_funcs), device=device)

        # Repeat all input columns (but "prompt", "completion", and "completion_ids") to match the num of generations
        keys = [key for key in inputs[0] if key not in ["prompt", "completion", "completion_ids"]]
        reward_kwargs = {key: [example[key] for example in inputs] for key in keys}

        # This allows for dynamic reward shaping based on training progress.
        reward_kwargs["trainer_state"] = self.state

        # Allow reward functions to log extra columns to the completions table.
        reward_kwargs["log_extra"] = self._log_completion_extra

        # Allow reward functions to log additional scalar metrics.
        reward_kwargs["log_metric"] = self._log_metric

        async_funcs_info = []  # async custom functions for asyncio.gather

        for i, (reward_func, reward_processing_class, reward_func_name) in enumerate(
            zip(self.reward_funcs, self.reward_processing_classes, self.reward_func_names, strict=True)
        ):
            if isinstance(reward_func, nn.Module):  # Module (no PretrainedModel) for compat with compiled models
                with profiling_context(self, reward_func_name):
                    if is_conversational(inputs[0]):
                        messages = [{"messages": p + c} for p, c in zip(prompts, completions, strict=True)]
                        texts = [
                            apply_chat_template(x, reward_processing_class, **self.chat_template_kwargs)["text"]
                            for x in messages
                        ]
                    else:
                        texts = [p + c for p, c in zip(prompts, completions, strict=True)]
                    reward_inputs = reward_processing_class(
                        text=texts, return_tensors="pt", padding=True, padding_side="right", add_special_tokens=False
                    )
                    reward_inputs = super()._prepare_inputs(reward_inputs)
                    with torch.inference_mode():
                        rewards_per_func[:, i] = reward_func(**reward_inputs).logits[:, 0]  # Shape (B*G,)
            elif inspect.iscoroutinefunction(reward_func):  # Separate async reward funcs to run them in parallel later
                async_funcs_info.append((i, reward_func, reward_func_name))
            else:
                # Run synchronous reward function
                with profiling_context(self, reward_func_name):
                    if self.environments is not None:
                        reward_kwargs["environments"] = self.environments
                    output_reward_func = reward_func(
                        prompts=prompts, completions=completions, completion_ids=completion_ids_list, **reward_kwargs
                    )
                    # Convert None values to NaN
                    output_reward_func = [reward if reward is not None else torch.nan for reward in output_reward_func]
                    rewards_per_func[:, i] = torch.tensor(output_reward_func, dtype=torch.float32, device=device)

        # Execute async custom functions in parallel using asyncio.gather
        if async_funcs_info:

            async def _invoke_async(index, func, func_name):
                with profiling_context(self, func_name):
                    output = await func(
                        prompts=prompts, completions=completions, completion_ids=completion_ids_list, **reward_kwargs
                    )
                    output = [r if r is not None else torch.nan for r in output]
                    return index, output

            async def _run_async_funcs():
                coros = [_invoke_async(i, func, func_name) for (i, func, func_name) in async_funcs_info]
                return await asyncio.gather(*coros)

            async_results = asyncio.run_coroutine_threadsafe(_run_async_funcs(), self.async_loop).result()
            for idx, output_reward_func in async_results:
                rewards_per_func[:, idx] = torch.tensor(output_reward_func, dtype=torch.float32, device=device)

        # If all reward functions return None for a given row, issue a detailed warning
        if torch.isnan(rewards_per_func).all(dim=1).any():
            nan_row_idx = torch.isnan(rewards_per_func).all(dim=1).nonzero(as_tuple=True)[0][0]
            row_reward_kwargs = {
                key: value[nan_row_idx]
                for key, value in reward_kwargs.items()
                if key not in ("trainer_state", "log_extra", "log_metric")
            }
            row_reward_kwargs["prompt"] = prompts[nan_row_idx]
            row_reward_kwargs["completion"] = completions[nan_row_idx]
            logger.warning(
                f"All reward functions returned None for the following kwargs:\n{row_reward_kwargs}\n"
                "Please ensure that at least one reward function returns a valid reward."
            )

        # Gather the reward per function: this part is crucial, because the rewards are normalized per group and the
        # completions may be distributed across processes
        rewards_per_func = gather(rewards_per_func)
        return rewards_per_func

    def _tokenize_prompts(self, prompts: list):
        """Tokenize prompts and extract images/multimodal fields for generation."""
        if is_conversational({"prompt": prompts[0]}):
            # Normalize string content to content blocks for VLM processors that don't handle plain strings.
            if self._is_vlm:
                prompts = [prepare_multimodal_messages(prompt) for prompt in prompts]

            # Extract images from messages for VLM support
            images = []
            has_images = False
            for prompt in prompts:
                prompt_images = []
                for message in prompt:
                    if isinstance(message["content"], list):
                        for part in message["content"]:
                            if part["type"] == "image":
                                prompt_images.append(part["image"])
                                has_images = True
                images.append(prompt_images if prompt_images else None)
            images = images if has_images else None

            # Workaround for a bug in transformers 5.3.0 where some processors (e.g. Qwen2.5-VL) crash on
            # batched unpadded input (transformers#44514).
            # Fixed in transformers 5.4.0 (transformers#44563).
            needs_padding_workaround = Version("5.3.0") <= Version(transformers.__version__) < Version("5.4.0")
            tokenized = self.processing_class.apply_chat_template(
                conversation=prompts,
                tools=self.tools or None,  # `or None`: Llama bug: it renders tool boilerplate for tools=[]
                chat_template=self.chat_template,
                add_generation_prompt=True,
                tokenize=True,
                return_dict=True,
                **({"padding": True} if needs_padding_workaround else {}),
                **self.chat_template_kwargs,
            )
            if needs_padding_workaround:
                # Unpad input_ids: remove padding tokens using attention_mask to get per-sequence lists
                prompt_ids = [
                    [tok for tok, m in zip(ids, mask, strict=True) if m]
                    for ids, mask in zip(tokenized["input_ids"], tokenized["attention_mask"], strict=True)
                ]
            else:
                prompt_ids = tokenized["input_ids"]
            # For VLMs, the processor returns extra multimodal fields (pixel_values, image_grid_thw, etc.)
            multimodal_fields = {k: v for k, v in tokenized.items() if k not in ("input_ids", "attention_mask")}
        else:
            prompt_ids = self.processing_class(text=prompts)["input_ids"]
            images = None
            multimodal_fields = {}
        return prompt_ids, images, multimodal_fields

    def _generate_single_turn(self, prompt_ids, images, multimodal_fields):
        device = self.accelerator.device
        mode = "train" if self.model.training else "eval"

        # Generate completions using either vLLM or regular generation
        if self.use_vllm:
            # Sync weights if training step changed
            if self.state.global_step != self._last_loaded_step:
                if not getattr(getattr(self.vllm_generation, 'llm', None), 'shared_weights', False):
                    with profiling_context(self, 'sync_weights'):
                        self.vllm_generation.sync_weights()
                self._last_loaded_step = self.state.global_step

            # Generate using vLLM with raw token IDs
            num_generations = self.num_generations if mode == "train" else self.num_generations_eval
            _, completion_ids, logprobs, _ = self.vllm_generation.generate(
                prompts=prompt_ids,
                images=images,
                num_generations=num_generations,
                profiler=profiling_context(self, "vLLM.generate"),
            )
            # vLLM returns per-token top-k logprobs; keep only the top-1 (sampled token) logprob
            logprobs = [[lp[0] for lp in seq] for seq in logprobs]

        elif self.use_transformers_continuous_batching:
            with (
                profiling_context(self, "transformers.generate_batch"),
                unwrap_model_for_generation(
                    self.model_wrapped, self.accelerator, gather_deepspeed3_params=self.args.ds3_gather_for_generation
                ) as unwrapped_model,
                torch.no_grad(),
                self._dist.summon_full_params(self.model_wrapped, recurse=False),
            ):
                # Cast to the appropriate dtype based on training configuration
                if self.args.bf16:
                    unwrapped_model.to(torch.bfloat16)
                elif self.args.fp16:
                    unwrapped_model.to(torch.float16)
                if self.args.cast_lm_head_to_fp32:
                    unwrapped_model.lm_head.to(torch.float32)
                all_outputs = unwrapped_model.generate_batch(
                    prompt_ids,
                    generation_config=self.generation_config,
                    continuous_batching_config=self.continuous_batching_config,
                    progress_bar=False,
                )
                unwrapped_model.train()
            completion_ids = [output.generated_tokens for output in all_outputs.values()]
            logprobs = None

        else:
            # Regular generation path: left-pad token IDs into tensors
            prompt_tensors = [torch.tensor(ids) for ids in prompt_ids]
            padded_ids = pad(prompt_tensors, padding_value=self._tokenizer.pad_token_id, padding_side="left")
            attention_mask = pad([torch.ones_like(t) for t in prompt_tensors], padding_value=0, padding_side="left")
            generate_inputs = {"input_ids": padded_ids, "attention_mask": attention_mask}
            # For VLMs, include multimodal fields as tensors (pixel_values, image_grid_thw, etc.)
            for k, v in multimodal_fields.items():
                if isinstance(v, torch.Tensor):
                    generate_inputs[k] = v
                elif isinstance(v, list) and v and isinstance(v[0], list):
                    # Per-token field (e.g., token_type_ids): left-pad like input_ids
                    generate_inputs[k] = pad([torch.tensor(x) for x in v], padding_value=0, padding_side="left")
                else:
                    generate_inputs[k] = torch.tensor(np.array(v))
            generate_inputs = super()._prepare_inputs(generate_inputs)
            if "mm_token_type_ids" in generate_inputs or "image_grid_thw" in generate_inputs:
                mm_token_type_ids = _unsloth_fix_mm_token_type_ids(
                    self.processing_class,
                    generate_inputs["input_ids"],
                    generate_inputs.get("mm_token_type_ids", None),
                )
                if mm_token_type_ids is not None:
                    generate_inputs["mm_token_type_ids"] = mm_token_type_ids

            with (
                profiling_context(self, "transformers.generate"),
                unwrap_model_for_generation(
                    self.model_wrapped,
                    self.accelerator,
                    gather_deepspeed3_params=self.args.ds3_gather_for_generation,
                    generation_kwargs=self.generation_kwargs,  # Override model.generation_config with generation_kwargs to fix transformers#42762
                ) as unwrapped_model,
                torch.no_grad(),
                self._dist.summon_full_params(self.model_wrapped, recurse=False),
            ):
                prompt_completion_ids = unwrapped_model.generate(
                    **generate_inputs, generation_config=self.generation_config
                )
            # Compute prompt length and extract completion ids
            prompt_length = generate_inputs["input_ids"].size(1)
            completion_ids = prompt_completion_ids[:, prompt_length:]

            # Mask everything after the first EOS token
            is_eos = completion_ids == self._tokenizer.eos_token_id
            eos_idx = torch.full((is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=device)
            eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)]
            sequence_indices = torch.arange(is_eos.size(1), device=device).expand(is_eos.size(0), -1)
            completion_mask = (sequence_indices <= eos_idx.unsqueeze(1)).int()
            completion_ids = [
                c[m].tolist() for c, m in zip(completion_ids.cpu(), completion_mask.bool().cpu(), strict=True)
            ]
            logprobs = None  # not used in this case

        return completion_ids, logprobs

    def _get_tool_suffix_ids(self, tool_messages):
        """Get token IDs for tool result formatting by using a minimal dummy conversation."""
        # Use the real tool name instead of a dummy: some templates (e.g. GPT-OSS) derive the tool response
        # header from the assistant's tool call name.
        dummy_tool_calls = [{"type": "function", "function": {"name": tool_messages[0]["name"], "arguments": {}}}]
        dummy_messages = [
            {"role": "user", "content": "dummy"},
            {
                "role": "assistant",
                # "content" is required here because VLM processors crash on tokenize=True without it
                # (KeyError in processing_utils.py). See huggingface/transformers#45290.
                "content": "",
                "tool_calls": dummy_tool_calls,
            },
        ]
        if self._is_vlm:
            dummy_messages = prepare_multimodal_messages(dummy_messages)
            tool_messages = prepare_multimodal_messages(tool_messages)

        prefix_ids = self.processing_class.apply_chat_template(
            dummy_messages,
            add_generation_prompt=False,
            tokenize=True,
            chat_template=self.chat_template,
            return_dict=False,
            **self.chat_template_kwargs,
        )
        full_ids = self.processing_class.apply_chat_template(
            dummy_messages + tool_messages,
            add_generation_prompt=True,
            tokenize=True,
            chat_template=self.chat_template,
            return_dict=False,
            **self.chat_template_kwargs,
        )
        # VLM processors return batched output (list of lists), unbatch for single conversation
        if self._is_vlm:
            prefix_ids = prefix_ids[0]
            full_ids = full_ids[0]

        # Some chat templates (notably Qwen3/Qwen3.5) render "...<|im_end|>\n" after an assistant/tool block.
        # When we compute `suffix_ids` by slicing `full_ids`, we must align the slicing boundary to
        # EOS (not EOS + newline). Templates that don't use EOS as end-of-turn (e.g. Gemma uses
        # <turn|>) skip this trimming.
        eos_positions = [i for i, tok_id in enumerate(prefix_ids) if tok_id == self._tokenizer.eos_token_id]
        if eos_positions:
            prefix_ids = prefix_ids[: eos_positions[-1] + 1]

        if full_ids[: len(prefix_ids)] != prefix_ids:
            raise ValueError("Unexpected tokenization: the EOS-trimmed prefix IDs are not a prefix of the full IDs.")
        return full_ids[len(prefix_ids) :]

    def _tool_call_loop(self, prompts, prompt_ids, completion_ids, completions, logprobs, images, multimodal_fields):
        # Tool execution loop: execute tools, then regenerate completions with tool results appended to the prompt
        tool_calls = [completion[0].get("tool_calls") for completion in completions]
        idxs_with_tool = [idx for idx, tool_call in enumerate(tool_calls) if tool_call]
        tool_calls = [tool_calls[idx] for idx in idxs_with_tool]
        tool_mask = [[1] * len(ids) for ids in completion_ids]  # 0 for tool result tokens, 1 elsewhere
        # Collect images from multimodal tool responses for the forward pass
        tool_images = [[] for _ in completion_ids]
        tool_call_count = 0
        tool_failure_count = 0
        iteration_num = 0

        while idxs_with_tool and iteration_num < self.max_tool_calling_iterations:
            prompt_completion_tools = [prompts[i] for i in idxs_with_tool]  # select only prompts that need tool calls
            # Snapshot state so we can rollback tool results that would exceed max_completion_length
            completions_len_before = [len(completions[i]) for i in idxs_with_tool]
            tool_images_len_before = [len(tool_images[i]) for i in idxs_with_tool]
            prompts_len_before = [len(prompts[i]) for i in idxs_with_tool]

            # Call the tools, and build the new prompt for generation
            for idx in range(len(idxs_with_tool)):
                idx_with_tool = idxs_with_tool[idx]
                tool_call_list = tool_calls[idx]
                prompt_completion_tool = prompt_completion_tools[idx]
                sync_tool_dict = self._sync_tool_dicts[idx_with_tool]
                async_tool_dict = self._async_tool_dicts[idx_with_tool]
                # Append the last assistant message (which triggered tool_calls) to the prompt
                prompt_completion_tool.append(completions[idx_with_tool][-1])
                async_coros = []
                tool_call_results = []
                for tool_call in tool_call_list:
                    tool_call_count += 1
                    if tool_call["type"] == "function":
                        function = tool_call["function"]
                        name = function["name"]
                        try:
                            if name in sync_tool_dict:
                                tool_call_results.append((name, sync_tool_dict[name](**function["arguments"])))
                            elif name in async_tool_dict:
                                async_coros.append((name, async_tool_dict[name](**function["arguments"])))
                            else:
                                raise ValueError(f"Tool {name} not found.")
                        except Exception as e:
                            tool_failure_count += 1
                            result = {"error": str(e)}
                            tool_call_results.append((name, result))
                    else:
                        tool_failure_count += 1
                        name = tool_call.get("name", "unknown")
                        tool_call_results.append((name, {"error": f"Unsupported tool call type: {tool_call['type']}"}))

                if async_coros:

                    async def _run_async_tools(async_coros):
                        coros = [coro for _, coro in async_coros]
                        results = await asyncio.gather(*coros, return_exceptions=True)
                        return [(name, result) for (name, _), result in zip(async_coros, results, strict=False)]

                    async_results = asyncio.run_coroutine_threadsafe(
                        _run_async_tools(async_coros), self.async_loop
                    ).result()

                    for name, result in async_results:
                        if isinstance(result, Exception):
                            tool_failure_count += 1
                            tool_call_results.append((name, {"error": str(result)}))
                        else:
                            tool_call_results.append((name, result))

                for name, result in tool_call_results:
                    # Support multimodal tool responses: if the tool returns a list of content blocks
                    # (e.g., [{"type": "image", "image": ...}, {"type": "text", "text": "..."}]),
                    # pass them through directly so _tokenize_prompts can extract images for VLMs.
                    content = result if isinstance(result, list) else str(result)
                    tool_message = {"role": "tool", "name": name, "content": content}
                    # Collect images from multimodal tool responses
                    if isinstance(content, list):
                        for part in content:
                            if isinstance(part, dict) and part.get("type") == "image":
                                tool_images[idx_with_tool].append(part["image"])
                    prompt_completion_tool.append(tool_message)
                    completions[idx_with_tool].append(tool_message)

            # Build token IDs by concatenation: prompt + completion + tool_suffix.
            prompt_completion_tool_ids = []
            for idx in range(len(idxs_with_tool)):
                idx_with_tool = idxs_with_tool[idx]
                # Extract trailing tool messages from completions
                tool_messages = []
                for message in reversed(completions[idx_with_tool]):
                    if message["role"] == "tool":
                        tool_messages.insert(0, message)
                    else:
                        break
                suffix_ids = self._get_tool_suffix_ids(tool_messages)
                prompt_completion_tool_ids.append(
                    prompt_ids[idx_with_tool] + completion_ids[idx_with_tool] + suffix_ids
                )

            # Drop tool results whose addition would push the sequence past max_completion_length (the completion
            # budget) or past the backend context ceiling (vLLM and transformers will error out on inputs longer than
            # the model's max length). The sample exits the loop with its completion as-is, and the tool
            # messages/images appended this iteration are rolled back so completions and tool_images stay consistent
            # with completion_ids.
            if self.use_vllm and self.vllm_mode == "colocate":
                max_model_len = self.vllm_generation.llm.llm_engine.model_config.max_model_len
            else:
                config = self.model.config.text_config if self._is_vlm else self.model.config
                max_model_len = config.max_position_embeddings
            overlong = [
                len(pct) - len(prompt_ids[i]) > self.max_completion_length or len(pct) >= max_model_len
                for i, pct in zip(idxs_with_tool, prompt_completion_tool_ids, strict=True)
            ]
            for idx in range(len(idxs_with_tool)):
                if overlong[idx]:
                    idx_with_tool = idxs_with_tool[idx]
                    del completions[idx_with_tool][completions_len_before[idx] :]
                    del tool_images[idx_with_tool][tool_images_len_before[idx] :]
                    del prompts[idx_with_tool][prompts_len_before[idx] :]
            # Keep only non-overlong items for further processing
            idxs_with_tool = [idx for idx, o in zip(idxs_with_tool, overlong, strict=True) if not o]
            prompt_completion_tool_ids = [
                pct for pct, o in zip(prompt_completion_tool_ids, overlong, strict=True) if not o
            ]
            if not idxs_with_tool:
                break  # all overlong, exit tool loop

            # Filter images and multimodal fields to match the current subset (index into full batch).
            # Merge tool response images so the model can see visual feedback during generation.
            merged_images = images
            if any(imgs for imgs in tool_images):
                if merged_images is None:
                    merged_images = [imgs if imgs else None for imgs in tool_images]
                else:
                    merged_images = [
                        (existing or []) + new for existing, new in zip(merged_images, tool_images, strict=True)
                    ]
            loop_images = [merged_images[i] for i in idxs_with_tool] if merged_images else None
            if multimodal_fields:
                loop_multimodal_fields = {}
                for k, v in multimodal_fields.items():
                    selected = [v[i] for i in idxs_with_tool]
                    # Per-token fields (e.g. token_type_ids) need zero-padding to match extended prompt length
                    if isinstance(selected[0], list):
                        selected = [
                            s + [0] * (len(pct) - len(s))
                            for s, pct in zip(selected, prompt_completion_tool_ids, strict=True)
                        ]
                    loop_multimodal_fields[k] = selected
            else:
                loop_multimodal_fields = {}

            # Generate new completions after tool execution (using concatenated IDs, no re-tokenization)
            post_tool_ids, post_tool_logprobs = self._generate_single_turn(
                prompt_completion_tool_ids, loop_images, loop_multimodal_fields
            )

            # Truncate so that pct[len(prompt_ids[idx]) :] + post_tool does not exceed max_completion_length.
            # The pre-regen check guarantees len(completion_tool_ids) <= max_completion_length, so any
            # excess can only come from post_tool_ids. post_tool_ids is model-generated text and never
            # contains image tokens, so a plain slice is safe.
            for idx in range(len(idxs_with_tool)):
                idx_with_tool = idxs_with_tool[idx]
                completion_tool_length = len(prompt_completion_tool_ids[idx]) - len(prompt_ids[idx_with_tool])
                excess_length = completion_tool_length + len(post_tool_ids[idx]) - self.max_completion_length
                if excess_length > 0:
                    new_len = len(post_tool_ids[idx]) - excess_length
                    post_tool_ids[idx] = post_tool_ids[idx][:new_len]
                    if logprobs is not None:
                        post_tool_logprobs[idx] = post_tool_logprobs[idx][:new_len]

            # Update tool_mask: the tool result should be 0 and the post-tool 1
            for idx in range(len(idxs_with_tool)):
                idx_with_tool = idxs_with_tool[idx]
                prompt_completion_tool_length = len(prompt_completion_tool_ids[idx])
                prompt_length = len(prompt_ids[idx_with_tool])
                completion_length = len(completion_ids[idx_with_tool])
                post_tool_length = len(post_tool_ids[idx])
                tool_length = prompt_completion_tool_length - prompt_length - completion_length
                tool_mask[idx_with_tool] += [0] * tool_length + [1] * post_tool_length
                if logprobs is not None:
                    logprobs[idx_with_tool] += [0.0] * tool_length + post_tool_logprobs[idx]

            # Update completion_ids with the new completions (after tool execution)
            for idx in range(len(idxs_with_tool)):
                idx_with_tool = idxs_with_tool[idx]
                prompt_length = len(prompt_ids[idx_with_tool])
                pct = prompt_completion_tool_ids[idx]  # = prompt-completion-tool
                completion_ids[idx_with_tool] = pct[prompt_length:] + post_tool_ids[idx]

            # Decode post-tool completions.
            post_tool_completions = [parse_response(self._tokenizer, ids) if ids else {} for ids in post_tool_ids]

            # Add post-tool completions to the existing completions
            for idx in range(len(idxs_with_tool)):
                idx_with_tool = idxs_with_tool[idx]
                if post_tool_completions[idx]:  # {} if post-tool completions completely truncated
                    completions[idx_with_tool].append(post_tool_completions[idx])

            # Check for further tool calls
            tool_calls = [completion.get("tool_calls") for completion in post_tool_completions]
            idxs_with_tool = [idx for idx, tool_call in zip(idxs_with_tool, tool_calls, strict=True) if tool_call]
            tool_calls = [tool_call for tool_call in tool_calls if tool_call]
            iteration_num += 1

        return tool_mask, completions, completion_ids, logprobs, tool_call_count, tool_failure_count, tool_images

    def _generate(self, prompts: list):
        device = self.accelerator.device
        mode = "train" if self.model.training else "eval"

        # Copy the prompts to avoid modifying the original list
        prompts = copy.deepcopy(prompts)

        if self.rollout_func is not None:
            # Keep vLLM weights in sync for custom rollouts that rely on vLLM utilities.
            if self.use_vllm and self.state.global_step != self._last_loaded_step:
                if not getattr(getattr(self.vllm_generation, 'llm', None), 'shared_weights', False):
                    with profiling_context(self, 'sync_weights'):
                        self.vllm_generation.sync_weights()
                self._last_loaded_step = self.state.global_step

            # Pass prompts to rollout_func preserving structured messages.
            # Chat templating must happen inside rollout_func, at the backend boundary, so that
            # multimodal content (images, typed content blocks) is not lost before rollout logic runs.
            output = self.rollout_func(prompts, self)
            required_keys = {"prompt_ids", "completion_ids", "logprobs"}
            missing_keys = required_keys - output.keys()
            if missing_keys:
                missing_keys_list = sorted(missing_keys)
                raise ValueError(f"rollout_func must return keys {missing_keys_list} in its output dict.")
            extra_fields = {k: v for k, v in output.items() if k not in required_keys}
            prompt_ids, completion_ids, logprobs = output["prompt_ids"], output["completion_ids"], output["logprobs"]
            images = None
            multimodal_fields = {}
        else:
            prompt_ids, images, multimodal_fields = self._tokenize_prompts(prompts)
            completion_ids, logprobs = self._generate_single_turn(prompt_ids, images, multimodal_fields)
            extra_fields = {}

        # Decode completions. It's important to use `parse_response` when possible, because it handles tool calls.
        if is_conversational({"prompt": prompts[0]}):
            if (
                Version(transformers.__version__) >= Version("5.0.0")  # parse_response added in v5
                and hasattr(self._tokenizer, "response_schema")  # attribute not set by default for now
                and self._tokenizer.response_schema is not None  # only works if the tokenizer has a schema
            ):
                completions = [[parse_response(self._tokenizer, ids)] for ids in completion_ids]
            else:
                contents = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True)
                completions = [[{"role": "assistant", "content": content}] for content in contents]
        else:
            completions = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True)

        # Extract tool calls from the completions and (possibly) execute them
        tool_images = []
        if self.tools:
            (
                tool_mask,
                completions,
                completion_ids,
                logprobs,
                tool_call_count,
                tool_failure_count,
                tool_images,
            ) = self._tool_call_loop(
                prompts, prompt_ids, completion_ids, completions, logprobs, images, multimodal_fields
            )
            # Merge tool response images into the images list for the forward pass
            if any(imgs for imgs in tool_images):
                if images is None:
                    images = [imgs if imgs else None for imgs in tool_images]
                else:
                    images = [(existing or []) + new for existing, new in zip(images, tool_images, strict=True)]
        else:
            # Support custom env_mask from rollout_func (e.g., for environment feedback masking)
            # Internally treated as tool_mask - marks model tokens (1) vs external tokens (0)
            tool_mask = extra_fields.pop("env_mask", None)

        # Get completion length per sequence, used for logging
        prompt_lengths = torch.tensor([len(ids) for ids in prompt_ids], device=device)
        if tool_mask is not None:  # count only model-generated tokens (tool_mask=1)
            completion_lengths = torch.tensor([sum(mask) for mask in tool_mask], device=device)
        else:
            completion_lengths = torch.tensor([len(ids) for ids in completion_ids], device=device)
        agg_prompt_lengths = self.accelerator.gather(prompt_lengths)
        agg_completion_lengths = self.accelerator.gather(completion_lengths)
        total_prompt_tokens = agg_prompt_lengths.sum()
        total_completion_tokens = agg_completion_lengths.sum()  # = num_items_in_batch, required for the DAPO loss

        # Log the metrics
        if mode == "train":
            self.state.num_input_tokens_seen += (total_prompt_tokens + total_completion_tokens).item()
        self._metrics[mode]["num_tokens"] = [self.state.num_input_tokens_seen]

        # Log completion lengths, mean, min, max
        self._metrics[mode]["completions/mean_length"].append(agg_completion_lengths.float().mean().item())
        self._metrics[mode]["completions/min_length"].append(agg_completion_lengths.float().min().item())
        self._metrics[mode]["completions/max_length"].append(agg_completion_lengths.float().max().item())

        # Identify sequences that terminated with EOS and log their lengths
        eos_and_pad = [self._tokenizer.eos_token_id, self._tokenizer.pad_token_id]
        is_truncated = torch.tensor([ids[-1] not in eos_and_pad for ids in completion_ids], device=device)
        agg_is_truncated = self.accelerator.gather(is_truncated)
        self._metrics[mode]["completions/clipped_ratio"].append(agg_is_truncated.float().mean().item())
        term_completion_lengths = agg_completion_lengths[~agg_is_truncated]
        if len(term_completion_lengths) == 0:  # edge case where no terminated sequences are found
            term_completion_lengths = torch.zeros(1, device=device)
        self._metrics[mode]["completions/mean_terminated_length"].append(term_completion_lengths.float().mean().item())
        self._metrics[mode]["completions/min_terminated_length"].append(term_completion_lengths.float().min().item())
        self._metrics[mode]["completions/max_terminated_length"].append(term_completion_lengths.float().max().item())

        if self.tools:
            agg_tool_call_count = self.accelerator.gather(torch.tensor(tool_call_count, device=device)).sum()
            tool_call_frequency = (agg_tool_call_count / len(agg_prompt_lengths)).item()
            self._metrics[mode]["tools/call_frequency"].append(tool_call_frequency)
            agg_tool_failure_count = self.accelerator.gather(torch.tensor(tool_failure_count, device=device)).sum()
            failure_frequency = (
                (agg_tool_failure_count / agg_tool_call_count).item() if agg_tool_call_count > 0 else 0.0
            )
            self._metrics[mode]["tools/failure_frequency"].append(failure_frequency)

        return (
            prompt_ids,
            completion_ids,
            tool_mask,
            completions,
            total_completion_tokens,
            logprobs,
            extra_fields,
            images,
            tool_images,
        )

    def _generate_and_score_completions(
        self, inputs: list[dict[str, torch.Tensor | Any]]
    ) -> dict[str, torch.Tensor | Any]:
        device = self.accelerator.device
        mode = "train" if self.model.training else "eval"

        prompts = [x["prompt"] for x in inputs]
        # Unsloth: Extract per-sample chat_template_kwargs before metadata is lost
        _ct_ = getattr(self.processing_class, 'chat_template', None) or ''
        _sk_ = {'prompt', 'chosen', 'rejected', 'completion', 'messages', 'label',
                'images', 'image', 'videos', 'video', 'audios', 'audio'}
        self._unsloth_batch_chat_kwargs = []
        for _inp_ in inputs:
            _kw_ = {}
            if isinstance(_inp_, dict):
                for _k_ in _inp_.keys() - _sk_:
                    if _k_ in _ct_ and isinstance(_inp_[_k_], str):
                        _kw_[_k_] = _inp_[_k_]
            self._unsloth_batch_chat_kwargs.append(_kw_)
        if self.environments:
            for prompt, environment, reset_kwargs in zip(prompts, self.environments, inputs, strict=True):
                observation = environment.reset(**reset_kwargs)
                if observation is None:
                    continue
                if isinstance(observation, list) and isinstance(prompt[-1]["content"], str):
                    prompt[-1]["content"] = [{"type": "text", "text": prompt[-1]["content"]}]
                if isinstance(observation, str) and isinstance(prompt[-1]["content"], list):
                    observation = [{"type": "text", "text": observation}]
                prompt[-1]["content"] += observation

        if "images" in inputs[0]:
            images = [example.get("images") for example in inputs]
        elif "image" in inputs[0]:
            images = [[example.get("image")] if example.get("image") is not None else None for example in inputs]
        else:
            images = None
        # Transformers requires at least one image in the batch, otherwise it throws an error
        if images is not None and all(img_list == [] for img_list in images):
            images = None

        # If the prompts are conversational and the inputs contain images, we need to convert the prompts from
        # [{"role": "user", "content": "What color is the sky?"}] to
        # [{"role": "user", "content": [{"type": "image", "image": <Image>}, {"type": "text", "text": "What color is the sky?"}]}]
        if images is not None:
            if not is_conversational(inputs[0]):
                raise ValueError(
                    "Multimodal training requires conversational prompts. It looks like the dataset contains "
                    "non-conversational inputs, likely because a chat template was applied before passing the dataset "
                    "to the trainer. Please provide the raw conversational prompts and let the trainer apply the chat "
                    "template internally."
                )
            prompts = [
                prepare_multimodal_messages(prompt, images=image_list)
                for prompt, image_list in zip(prompts, images, strict=True)
            ]

        dataset_images = images  # preserve dataset images before _generate may overwrite
        (
            prompt_ids_list,
            completion_ids_list,
            tool_mask_list,
            completions,
            num_items_in_batch,
            sampling_per_token_logps_list,
            extra_fields,
            images,
            tool_images,
        ) = self._generate(prompts)

        _unsloth_clear_stateful_mrope(
            self.accelerator.unwrap_model(self.model, keep_fp32_wrapper = False)
        )
        if images is None:
            images = dataset_images  # restore dataset images (rollout_func path returns None)

        # Convert lists of token IDs to padded tensors
        prompt_ids = [torch.tensor(ids) for ids in prompt_ids_list]
        prompt_mask = [torch.ones_like(ids, dtype=torch.long) for ids in prompt_ids]
        prompt_ids = pad(
            prompt_ids,
            padding_value=self._tokenizer.pad_token_id,
            padding_side="left",
            pad_to_multiple_of=self.pad_to_multiple_of,
        ).to(device=device)
        prompt_mask = pad(
            prompt_mask, padding_value=0, padding_side="left", pad_to_multiple_of=self.pad_to_multiple_of
        ).to(device=device)
        completion_ids = [torch.tensor(ids) for ids in completion_ids_list]
        completion_mask = [torch.ones_like(ids, dtype=torch.long) for ids in completion_ids]
        completion_ids = pad(
            completion_ids,
            padding_value=self._tokenizer.pad_token_id,
            padding_side="right",
            pad_to_multiple_of=self.pad_to_multiple_of,
        ).to(device=device)
        completion_mask = pad(
            completion_mask, padding_value=0, padding_side="right", pad_to_multiple_of=self.pad_to_multiple_of
        ).to(device=device)
        if sampling_per_token_logps_list is not None:
            sampling_per_token_logps = [torch.tensor(logps) for logps in sampling_per_token_logps_list]
            sampling_per_token_logps = pad(
                sampling_per_token_logps,
                padding_value=0.0,
                padding_side="right",
                pad_to_multiple_of=self.pad_to_multiple_of,
            ).to(device=device)
        else:
            sampling_per_token_logps = None
        if tool_mask_list is not None:
            tool_mask = [torch.tensor(mask) for mask in tool_mask_list]
            tool_mask = pad(
                tool_mask, padding_value=1, padding_side="right", pad_to_multiple_of=self.pad_to_multiple_of
            ).to(device=device)
        else:
            tool_mask = None

        # If mask_truncated_completions is enabled, zero out truncated completions for attention and loss masking
        if self.mask_truncated_completions:
            eos_and_pad = [self._tokenizer.eos_token_id, self._tokenizer.pad_token_id]
            is_truncated = torch.tensor([ids[-1] not in eos_and_pad for ids in completion_ids_list], device=device)
            # Mask completion_mask for attention masking
            completion_mask = completion_mask * (~is_truncated).unsqueeze(1).int()
            # Also mask tool_mask for consistency in multi-turn training
            if tool_mask is not None:
                tool_mask = tool_mask * (~is_truncated).unsqueeze(1).int()

        # Concatenate prompt_mask with completion_mask for logit computation
        prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1)  # (B, P+C)
        attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)  # (B, P+C)

        logits_to_keep = completion_ids.size(1)  # we only need to compute the logits for the completion tokens
        
        max_left_pad = None
        batch_size = self.args.per_device_train_batch_size if mode == "train" else self.args.per_device_eval_batch_size
        try:
            # TRL 0.23.1 and below path
            if not has_images:
                # Left pad prompt before calculation old and ref hidden states
                left_pad_tokens_per_prompt = calculate_pad_tokens_in_prompt(prompt_completion_ids, logits_to_keep, self.processing_class.pad_token_id)
                max_left_pad = torch.max(left_pad_tokens_per_prompt).item()
        except:
            # TRL 0.24.0 and below path
            if images is None:
                # Left pad prompt before calculation old and ref hidden states
                left_pad_tokens_per_prompt = calculate_pad_tokens_in_prompt(prompt_completion_ids, logits_to_keep, self.processing_class.pad_token_id)
                max_left_pad = torch.max(left_pad_tokens_per_prompt).item()
        self.model.for_training(use_gradient_checkpointing=getattr(self.args, 'gradient_checkpointing', True))

        num_images = [len(img_list) if img_list else 0 for img_list in images] if images is not None else None

        # Get forward_kwargs for models with multimodal inputs.
        # When tool images are present (from _tool_call_loop), use image_processor directly and build
        # mm_token_type_ids from prompt_completion_ids. Otherwise, use the full processor pipeline
        # which returns model-specific keys (image_sizes, pixel_attention_mask, etc.).
        if self.tools and any(imgs for imgs in tool_images) and self._is_vlm:
            flat_images = [img for img_list in images if img_list for img in img_list]
            image_inputs = self.processing_class.image_processor(images=flat_images, return_tensors="pt")
            image_inputs = super()._prepare_inputs(image_inputs)
            forward_kwargs = dict(image_inputs)
        elif images is not None:
            prompts_text = [
                apply_chat_template(
                    {"prompt": prompt}, self.processing_class, tools=self.tools, **self.chat_template_kwargs
                )["prompt"]
                for prompt in prompts
            ]
            prompt_inputs = self.processing_class(images=images, text=prompts_text, padding=True, return_tensors="pt")
            prompt_inputs = super()._prepare_inputs(prompt_inputs)
            forward_kwargs = {k: v for k, v in prompt_inputs.items() if k not in ["input_ids", "attention_mask"]}
        else:
            forward_kwargs = {}

        # Recover LFM2-VL tile counts; the full processor drops row/column metadata.
        num_tiles = None
        if images is not None and "spatial_shapes" in forward_kwargs:
            image_info = self.processing_class.image_processor(
                images=images, return_tensors="pt", return_row_col_info=True
            )
            tiles_per_image = image_info["image_rows"] * image_info["image_cols"]
            if self.processing_class.image_processor.use_thumbnail:
                tiles_per_image = tiles_per_image + (tiles_per_image > 1).to(tiles_per_image.dtype)
            num_tiles = [group.sum().item() for group in torch.split(tiles_per_image, num_images)]

        # If token_type_ids are used, extend them with zeros for the completion part
        if "token_type_ids" in forward_kwargs:
            token_type_ids = forward_kwargs["token_type_ids"]
            if self.pad_to_multiple_of is not None:
                # Needed only with pad_to_multiple_of: otherwise prompt_ids and token_type_ids must have equal len
                padding_size = prompt_ids.size(1) - token_type_ids.size(1)
                if padding_size > 0:
                    token_type_ids = torch.cat(
                        [token_type_ids.new_zeros((token_type_ids.size(0), padding_size)), token_type_ids], dim=1
                    )
            forward_kwargs["token_type_ids"] = torch.cat(
                [token_type_ids, token_type_ids.new_zeros(completion_ids.shape)], dim=1
            )
        # If mm_token_type_ids are used, extend them with zeros for the completion part
        if "mm_token_type_ids" in forward_kwargs:
            mm_token_type_ids = forward_kwargs["mm_token_type_ids"]
            if self.pad_to_multiple_of is not None:
                # Needed only with pad_to_multiple_of: otherwise prompt_ids and mm_token_type_ids must have equal len
                padding_size = prompt_ids.size(1) - mm_token_type_ids.size(1)
                if padding_size > 0:
                    mm_token_type_ids = torch.cat(
                        [mm_token_type_ids.new_zeros((mm_token_type_ids.size(0), padding_size)), mm_token_type_ids],
                        dim=1,
                    )
            forward_kwargs["mm_token_type_ids"] = torch.cat(
                [mm_token_type_ids, mm_token_type_ids.new_zeros(completion_ids.shape)], dim=1
            )
        if "mm_token_type_ids" in forward_kwargs or "image_grid_thw" in forward_kwargs:
            _mm_token_type_ids = _unsloth_fix_mm_token_type_ids(
                self.processing_class,
                prompt_completion_ids,
                forward_kwargs.get("mm_token_type_ids", None),
                completion_ids = completion_ids,
            )
            if _mm_token_type_ids is not None:
                forward_kwargs["mm_token_type_ids"] = _mm_token_type_ids

        # For VLM tool images: build token type IDs from the full prompt_completion_ids.
        # This must happen AFTER the token_type_ids/mm_token_type_ids extension blocks above,
        # because our version already covers the full sequence (images are in the completion,
        # not just the prompt).
        if self.tools and any(imgs for imgs in tool_images) and self._is_vlm:
            mm_ids = torch.zeros_like(prompt_completion_ids)
            if self._image_pad_token_id is not None:
                mm_ids[prompt_completion_ids == self._image_pad_token_id] = 1
            if self._video_pad_token_id is not None:
                mm_ids[prompt_completion_ids == self._video_pad_token_id] = 2

            # Use the same key the model expects: token_type_ids for models like Gemma,
            # mm_token_type_ids for models like Qwen.
            image_grid_thw = forward_kwargs.get("image_grid_thw")
            if image_grid_thw is not None:
                forward_kwargs["mm_token_type_ids"] = mm_ids
            else:
                forward_kwargs["token_type_ids"] = mm_ids

            # Truncation safety (Qwen-style models with image_grid_thw only): if
            # max_completion_length truncated some image tokens, the number of image pad tokens
            # in input_ids won't match pixel_values features. Check per-sample and drop ALL
            # images for any sample with a mismatch (safe fallback).
            if image_grid_thw is not None and num_images is not None:
                merge_length = getattr(self.processing_class.image_processor, "merge_size", 2) ** 2
                img_offset = 0
                has_mismatch = False
                for b in range(mm_ids.shape[0]):
                    sample_tokens = (mm_ids[b] == 1).sum().item()
                    sample_features = 0
                    for i in range(num_images[b]):
                        grid_idx = img_offset + i
                        if grid_idx < image_grid_thw.shape[0]:
                            sample_features += image_grid_thw[grid_idx].prod().item() // merge_length
                    if sample_tokens != sample_features:
                        has_mismatch = True
                        break
                    img_offset += num_images[b]

                if has_mismatch:
                    # Drop all images: safer than partial trim which is error-prone
                    forward_kwargs.pop("pixel_values", None)
                    forward_kwargs.pop("image_grid_thw", None)
                    mm_ids.zero_()
                    forward_kwargs["mm_token_type_ids"] = mm_ids
                    num_images = None

        # When gradient checkpointing is enabled with use_reentrant=True (non default), calling the model inside a
        # torch.no_grad() block triggers a harmless PyTorch warning ("None of the inputs have requires_grad=True").
        # Temporarily disable checkpointing to avoid this warning during inference.
        with torch.no_grad(), disable_gradient_checkpointing(self.model, self.args.gradient_checkpointing_kwargs):
            # If the generation and optimization steps are misaligned—i.e., if generation does not occur at the end of
            # a full optimizer step (when gradient_accumulation_steps is not a multiple of generate_every)—then the
            # samples may come from an earlier version of the model. In that case, we need to track old_per_token_logps
            # for importance sampling. If the steps are aligned, importance sampling isn't necessary and we set
            # old_per_token_logps to None.
            # When using vLLM, we always compute old_per_token_logps for importance sampling, it was shown that the
            # distribution mismatch between vLLM and the training model can be large and harm the training.
            generate_every = self.args.steps_per_generation * self.num_iterations  # generation frequency

            if self.args.gradient_accumulation_steps % generate_every != 0 or (
                self.use_vllm
            ):
                old_per_token_logps, _, _ = self._get_per_token_logps_and_entropies(
                    self.model,
                    prompt_completion_ids,
                    attention_mask,
                    logits_to_keep,
                    batch_size,
                    num_images=num_images,
                    num_tiles=num_tiles,
                    **forward_kwargs,  # may contain pixel_values, image_grid_thw, pixel_attention_mask, spatial_shapes, image_sizes, image_position_ids
                )
            else:
                old_per_token_logps = None

            # Compute the importance sampling ratio when using vLLM, to correct for potential distribution mismatch
            if False and self.use_vllm and self.vllm_importance_sampling_correction:
                mask = completion_mask if tool_mask is None else completion_mask * tool_mask
                per_token_logps_diff = (old_per_token_logps - sampling_per_token_logps) * mask

                sequence_level_is = self.vllm_importance_sampling_mode in ["sequence_mask", "sequence_truncate"]
                if sequence_level_is:
                    per_sequence_logps_diff = per_token_logps_diff.sum(dim=-1, keepdim=True)
                    logps_diff = per_sequence_logps_diff
                else:
                    logps_diff = per_token_logps_diff

                vllm_importance_sampling_ratio = torch.exp(logps_diff)

                # vllm_importance_sampling_ratio.shape:
                #   token_* modes:     (B, T)  (per-token ratio)
                #   sequence_* modes:  (B, 1)  (per-sequence ratio)

                if self.vllm_importance_sampling_mode in ["sequence_truncate", "token_truncate"]:
                    vllm_importance_sampling_ratio = torch.clamp(
                        vllm_importance_sampling_ratio,
                        min=self.vllm_importance_sampling_clip_min,
                        max=self.vllm_importance_sampling_clip_max,
                    )
                elif self.vllm_importance_sampling_mode in ["sequence_mask", "token_mask"]:
                    min_val = (
                        self.vllm_importance_sampling_clip_min
                        if self.vllm_importance_sampling_clip_min is not None
                        else -math.inf
                    )
                    max_val = (
                        self.vllm_importance_sampling_clip_max
                        if self.vllm_importance_sampling_clip_max is not None
                        else math.inf
                    )

                    invalid_mis_mask = (vllm_importance_sampling_ratio < min_val) | (
                        vllm_importance_sampling_ratio > max_val
                    )
                    vllm_importance_sampling_ratio = vllm_importance_sampling_ratio.masked_fill(
                        invalid_mis_mask, value=0.0
                    )
                else:
                    raise ValueError(
                        f"Unknown vLLM importance sampling level: {self.vllm_importance_sampling_mode}. Possible values are 'token_truncate', 'token_mask', 'sequence_truncate', and 'sequence_mask'."
                    )

            # Compute the per-token log probabilities for the reference model
            if self.beta != 0.0:
                if self.ref_model is not None:
                    ref_per_token_logps, _, _ = self._get_per_token_logps_and_entropies(
                        self.ref_model,
                        prompt_completion_ids,
                        attention_mask,
                        logits_to_keep,
                        batch_size=batch_size,
                        num_images=num_images,
                        num_tiles=num_tiles,
                        **forward_kwargs,  # may contain pixel_values, image_grid_thw, pixel_attention_mask, spatial_shapes, image_sizes, image_position_ids
                    )
                else:
                    # When training a PEFT adapter, how we obtain the reference depends on the setup:
                    # - New adapter: disabling adapters yields the base model.
                    # - Re-training an existing adapter: an initial copy is loaded under the name "ref".
                    model = self.accelerator.unwrap_model(self.model)
                    with use_adapter(model, adapter_name="ref" if "ref" in model.peft_config else None):
                        ref_per_token_logps, _, _ = self._get_per_token_logps_and_entropies(
                            self.model,
                            prompt_completion_ids,
                            attention_mask,
                            logits_to_keep,
                            batch_size=batch_size,
                            num_images=num_images,
                            num_tiles=num_tiles,
                            **forward_kwargs,  # may contain pixel_values, image_grid_thw, pixel_attention_mask, spatial_shapes, image_sizes, image_position_ids
                        )
            else:
                ref_per_token_logps = None

        # Decode
        prompts_text = self.processing_class.batch_decode(prompt_ids, skip_special_tokens=True)
        completions_text = self.processing_class.batch_decode(completion_ids, skip_special_tokens=True)

        # Merge extra_fields from rollout_func into inputs for reward functions
        if extra_fields:
            for i, inp in enumerate(inputs):
                for key, values in extra_fields.items():
                    if isinstance(values, list) and i < len(values):
                        inp[key] = values[i]
                    elif not isinstance(values, list):
                        inp[key] = values

        # Calculate rewards for each reward function. rewards_per_func aggregates rewards across all processes. This is
        # important because rewards will be normalized per group, and completions are distributed. We will later slice
        # rewards_per_func to extract each process's subset.
        if images is not None:
            rewards_per_func = self._calculate_rewards(inputs, prompts_text, completions_text, completion_ids_list)
        else:
            rewards_per_func = self._calculate_rewards(inputs, prompts, completions, completion_ids_list)
        num_generations = self.num_generations if mode == "train" else self.num_generations_eval

        # A completion for which every reward function returned None is unscorable. nansum would collapse it to 0,
        # which both biases the per-group baseline and hands the completion a spurious advantage. Mark these rows NaN
        # so they're excluded from the (nan-aware) baseline below; their advantage is forced to 0 afterwards.
        unscorable_mask = torch.isnan(rewards_per_func).all(dim=1)

        if self.multi_objective_aggregation == "sum_then_normalize":
            # Apply weights to each reward function's output and sum
            rewards = (rewards_per_func * self.reward_weights.to(device).unsqueeze(0)).nansum(dim=1)
            rewards[unscorable_mask] = torch.nan
            mean_grouped_rewards = torch.nanmean(rewards.view(-1, num_generations), dim=1)
            mean_grouped_rewards = mean_grouped_rewards.repeat_interleave(num_generations, dim=0)
            if self.scale_rewards in ["group", "none"]:
                # If self.scale_rewards = "none", we'll only use std_rewards to check for zero std for logging
                if num_generations > 1:
                    std_rewards = nanstd(rewards.view(-1, num_generations), dim=1)
                    std_rewards = std_rewards.repeat_interleave(num_generations, dim=0)
                else:  # doesn't occur during training, but could occur in eval when num_generations_eval=1
                    std_rewards = torch.zeros_like(rewards)
            elif self.scale_rewards == "batch":
                # Compute global std
                if rewards.numel() > 1:
                    std_rewards = nanstd(rewards).expand_as(rewards)
                else:  # doesn't occur during training, but could occur in eval when num_generations_eval=batch_size=1
                    std_rewards = torch.zeros_like(rewards)
            else:
                raise ValueError(
                    f"Invalid value for scale_rewards: {self.scale_rewards}. Must be one of 'batch', 'group', or 'none'."
                )

            advantages = rewards - mean_grouped_rewards
            if self.scale_rewards != "none":
                advantages = advantages / (std_rewards + 1e-4)
            is_std_zero = torch.isclose(std_rewards, torch.zeros_like(std_rewards))  # for logging

        elif self.multi_objective_aggregation == "normalize_then_sum":
            grouped = rewards_per_func.view(-1, num_generations, len(self.reward_funcs))
            mean_k = torch.nanmean(grouped, dim=1, keepdim=True)
            std_k = nanstd(grouped, dim=1, keepdim=True) if num_generations > 1 else torch.zeros_like(mean_k)
            reward_k = (grouped - mean_k) / (std_k + 1e-4)
            reward_k = reward_k.view(-1, len(self.reward_funcs))
            rewards = (reward_k * self.reward_weights.to(device).unsqueeze(0)).nansum(dim=1)
            rewards[unscorable_mask] = torch.nan
            std_rewards = nanstd(rewards).expand_as(rewards) if rewards.numel() > 1 else torch.zeros_like(rewards)
            advantages = (rewards - torch.nanmean(rewards)) / (std_rewards + 1e-4)
            is_std_zero = torch.isclose(std_rewards, torch.zeros_like(std_rewards))  # for logging

        else:
            raise ValueError(
                f"Invalid multi_objective_aggregation: {self.multi_objective_aggregation}. Must be "
                "'sum_then_normalize' or 'normalize_then_sum'."
            )

        # Unscorable completions (every reward func returned None) carry no learning signal: their reward is NaN here,
        # so zero their advantage to keep them from moving the policy.
        advantages = torch.nan_to_num(advantages, nan=0.0)

        # Slice to keep only the local part of the data
        process_slice = slice(
            self.accelerator.process_index * len(prompts),
            (self.accelerator.process_index + 1) * len(prompts),
        )
        all_process_advantages = advantages.clone()  # keep the aggregated advantages for logging
        advantages = advantages[process_slice]

        # Calculate mean reward per function, but only for samples where the function was applied (non-NaN values)
        for i, reward_func_name in enumerate(self.reward_func_names):
            mean_rewards = torch.nanmean(rewards_per_func[:, i]).item()
            self._metrics[mode][f"rewards/{reward_func_name}/mean"].append(mean_rewards)
            std_func_rewards = nanstd(rewards_per_func[:, i]).item()
            self._metrics[mode][f"rewards/{reward_func_name}/std"].append(std_func_rewards)
        rewards = (rewards_per_func * self.reward_weights.to(rewards_per_func.device).unsqueeze(0)).nansum(dim=1)
        rewards[unscorable_mask] = torch.nan  # exclude unscorable rows from the logged reward stats
        self._metrics[mode]["reward"].append(torch.nanmean(rewards).item())
        self._metrics[mode]["reward_std"].append(nanstd(rewards).item())
        self._metrics[mode]["frac_reward_zero_std"].append(is_std_zero.float().mean().item())

        # Log prompt and completion texts
        self._logs["prompt"].extend(gather_object(prompts_text))
        self._logs["completion"].extend(gather_object(completions_text))
        for i, name in enumerate(self.reward_func_names):
            self._logs["rewards"][name].extend(rewards_per_func[:, i].tolist())
        self._logs["advantages"].extend(all_process_advantages.tolist())

        # Flush user-logged extra columns (from log_extra), gathering across processes.
        # Keys must be sorted so that all ranks call gather_object in the same order, otherwise values
        # get mis-attributed across columns (dict insertion order may differ between processes).
        for column in sorted(self._pending_extra_logs):
            self._logs["extra"][column].extend(gather_object(self._pending_extra_logs[column]))
        self._pending_extra_logs.clear()

        # Flush user-logged metrics (from log_metric), averaging across processes.
        # Keys must be sorted so that all ranks call accelerator.gather in the same order, otherwise values
        # get mis-attributed across metrics (dict insertion order may differ between processes).
        for name in sorted(self._pending_metrics):
            values = self._pending_metrics[name]
            local_mean = sum(values) / len(values)
            global_mean = self.accelerator.gather(torch.tensor(local_mean, device=device)).mean().item()
            self._metrics[mode][name].append(global_mean)
        self._pending_metrics.clear()

        if images is not None:
            self._logs["images"].extend(gather_object(images))

        if False and self.use_vllm and self.vllm_importance_sampling_correction:
            delta = torch.abs(old_per_token_logps - sampling_per_token_logps)
            mask = completion_mask.bool() if tool_mask is None else (completion_mask * tool_mask).bool()
            delta = delta[mask]
            mean_delta = torch.mean(delta) if delta.numel() > 0 else torch.tensor(0.0, device=device)
            max_delta = torch.max(delta) if delta.numel() > 0 else torch.tensor(0.0, device=device)
            self._metrics[mode]["sampling/sampling_logp_difference/mean"].append(
                self.accelerator.gather(mean_delta).mean().item()
            )
            self._metrics[mode]["sampling/sampling_logp_difference/max"].append(
                self.accelerator.gather(max_delta).max().item()
            )
            if sequence_level_is:
                flat_is_ratio = vllm_importance_sampling_ratio.flatten()
            else:
                flat_is_ratio = vllm_importance_sampling_ratio[mask]

            min_importance_sampling_ratio = (
                torch.min(flat_is_ratio) if flat_is_ratio.numel() > 0 else torch.tensor(0.0, device=device)
            )
            mean_importance_sampling_ratio = (
                torch.mean(flat_is_ratio) if flat_is_ratio.numel() > 0 else torch.tensor(0.0, device=device)
            )
            max_importance_sampling_ratio = (
                torch.max(flat_is_ratio) if flat_is_ratio.numel() > 0 else torch.tensor(0.0, device=device)
            )
            self._metrics[mode]["sampling/importance_sampling_ratio/min"].append(
                nanmin(self.accelerator.gather(min_importance_sampling_ratio)).item()
            )
            self._metrics[mode]["sampling/importance_sampling_ratio/mean"].append(
                self.accelerator.gather(mean_importance_sampling_ratio).nanmean().item()
            )
            self._metrics[mode]["sampling/importance_sampling_ratio/max"].append(
                nanmax(self.accelerator.gather(max_importance_sampling_ratio)).item()
            )

        output = {
            "prompt_ids": prompt_ids,
            "prompt_mask": prompt_mask,
            "completion_ids": completion_ids,
            "completion_mask": completion_mask,
            "advantages": advantages,
            "num_items_in_batch": num_items_in_batch,
        }
        if old_per_token_logps is not None:
            output["old_per_token_logps"] = old_per_token_logps
        if False and self.use_vllm and self.vllm_importance_sampling_correction:
            output["importance_sampling_ratio"] = vllm_importance_sampling_ratio
        if sampling_per_token_logps is not None:
            output["sampling_per_token_logps"] = sampling_per_token_logps
        if ref_per_token_logps is not None:
            output["ref_per_token_logps"] = ref_per_token_logps
        if "pixel_values" in forward_kwargs:
            output["pixel_values"] = forward_kwargs["pixel_values"]
        if "image_grid_thw" in forward_kwargs:
            output["image_grid_thw"] = forward_kwargs["image_grid_thw"]
        if "pixel_attention_mask" in forward_kwargs:
            output["pixel_attention_mask"] = forward_kwargs["pixel_attention_mask"]
        if "spatial_shapes" in forward_kwargs:
            output["spatial_shapes"] = forward_kwargs["spatial_shapes"]
        if "image_sizes" in forward_kwargs:
            output["image_sizes"] = forward_kwargs["image_sizes"]
        if "token_type_ids" in forward_kwargs:
            output["token_type_ids"] = forward_kwargs["token_type_ids"]
        if "mm_token_type_ids" in forward_kwargs:
            output["mm_token_type_ids"] = forward_kwargs["mm_token_type_ids"]
        if "image_position_ids" in forward_kwargs:
            output["image_position_ids"] = forward_kwargs["image_position_ids"]
        if images is not None:
            output["num_images"] = num_images
        if max_left_pad is not None:
            output["max_left_pad"] = torch.tensor(prompt_ids.shape[0] * [max_left_pad]).unsqueeze(-1)
        try:
            if self.use_vllm and getattr(self, "vllm_importance_sampling_correction", False):
                output["sampling_per_token_logps"] = sampling_per_token_logps
        except NameError:
            output["sampling_per_token_logps"] = None
            if num_tiles is not None:
                output["num_tiles"] = num_tiles
        if tool_mask is not None:
            output["tool_mask"] = tool_mask
        return output

    def compute_liger_loss(self, unwrapped_model, inputs):
        # Compute the per-token log probabilities for the model
        prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"]
        completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"]
        input_ids = torch.cat([prompt_ids, completion_ids], dim=1)
        attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)
        logits_to_keep = completion_ids.size(1)  # we only need to compute the logits for the completion tokens

        # Get the last hidden state of the model
        last_hidden_state = self._get_last_hidden_state(
            unwrapped_model,
            input_ids,
            attention_mask,
            logits_to_keep,
            inputs.get("pixel_values"),
            inputs.get("image_grid_thw"),
            inputs.get("pixel_attention_mask"),
            inputs.get("spatial_shapes"),
            inputs.get("image_sizes"),
            inputs.get("image_position_ids"),
        )

        # Apply tool_mask (from env_mask) for loss computation in multi-turn training scenarios
        loss_mask = completion_mask if "tool_mask" not in inputs else completion_mask * inputs["tool_mask"]
        lm_head_weight = unwrapped_model.lm_head.weight
        lm_head_bias = unwrapped_model.lm_head.bias
        # Liger reads `lm_head` directly instead of through `model.forward()`, so its ZeRO-3 gather hook never fires
        # and the fused matmul gets an empty shard. Gather the weight/bias ourselves for the call (the weight grad is
        # computed during this forward, so it isn't needed in the backward). Skip it when already gathered: with tied
        # embeddings `embed_tokens` keeps the weight `AVAILABLE`, and re-partitioning on exit breaks its tracking.
        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
        gather_ctx = nullcontext()
        if deepspeed_plugin is not None and deepspeed_plugin.zero_stage == 3:
            from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus

            params = [lm_head_weight] if lm_head_bias is None else [lm_head_weight, lm_head_bias]
            if any(p.ds_status != ZeroParamStatus.AVAILABLE for p in params):
                import deepspeed

                gather_ctx = deepspeed.zero.GatheredParameters(params, modifier_rank=None)
        with gather_ctx:
            loss, metrics = self.liger_grpo_loss(
                _input=last_hidden_state,
                lin_weight=lm_head_weight,
                selected_token_ids=completion_ids,
                # The attention_mask parameter in liger loss is actually used as a loss mask (not model attention)
                attention_mask=loss_mask,
                advantages=inputs["advantages"],
                bias=lm_head_bias,
                old_per_token_logps=inputs.get("old_per_token_logps"),
                ref_per_token_logps=inputs.get("ref_per_token_logps"),
                vllm_is_ratio=inputs.get("importance_sampling_ratio"),
            )
        # Extract metrics from the liger_grpo_loss output
        # KL divergence is the first metric when beta is non-zero
        mean_kl = metrics[0] if self.beta != 0.0 else None
        clip_ratio = metrics[-1]

        mode = "train" if self.model.training else "eval"
        if self.beta != 0.0:
            self._metrics[mode]["kl"].append(self.accelerator.gather(mean_kl).mean().item())
        self._metrics[mode]["clip_ratio"].append(self.accelerator.gather(clip_ratio).mean().item())
        normalizer = self.current_gradient_accumulation_steps if mode == "train" else 1.0  # no accum in eval
        return loss / normalizer

    def compute_loss(
        self,
        model,
        inputs,
        return_outputs = False,
        num_items_in_batch = None,
    ):
        if return_outputs:
            raise ValueError("The GRPOTrainer does not support returning outputs")
        # Compute the per-token log probabilities for the model

        prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"]
        completion_ids, completion_mask = (
            inputs["completion_ids"],
            inputs["completion_mask"],
        )
        pixel_values, image_grid_thw = (
            inputs.get("pixel_values", None),
            inputs.get("image_grid_thw", None),
        )
        pixel_attention_mask, image_sizes = (
            inputs.get("pixel_attention_mask", None),
            inputs.get("image_sizes", None),
        )
        num_images = inputs.get("num_images", None)
        # Transformers 5.x needs token_type_ids/mm_token_type_ids for some vision models
        token_type_ids = inputs.get("token_type_ids", None)
        mm_token_type_ids = inputs.get("mm_token_type_ids", None)
        num_items_in_batch = inputs.get("num_items_in_batch", None)
        sampling_per_token_logps = inputs.get("sampling_per_token_logps", None)
        tool_mask = inputs.get("tool_mask", None)
        # Missing when evaluate() runs standalone; eval does not accumulate, so
        # fall back to 1 to avoid underreporting eval_loss (#2464).
        current_gradient_accumulation_steps = getattr(
            self, "current_gradient_accumulation_steps", 1
        )
        num_processes = self.accelerator.num_processes

        input_ids = torch.cat([prompt_ids, completion_ids], dim = 1)
        bsz, qlen = input_ids.shape
        attention_mask = torch.cat([prompt_mask, completion_mask], dim = 1)
        if mm_token_type_ids is not None or image_grid_thw is not None:
            mm_token_type_ids = _unsloth_fix_mm_token_type_ids(
                self.processing_class,
                input_ids,
                mm_token_type_ids,
                completion_ids = completion_ids,
            )
        # attention_mask = None
        logits_to_keep = completion_ids.size(
            1
        )  # we only need to compute the logits for the completion tokens
        _input_ids = input_ids
        _logits_to_keep = logits_to_keep

        get_logps_func = (
            lambda model,
            input_ids,
            attention_mask,
            logits_to_keep,
            batch_size = None,
            compute_entropy = False,
            compute_efficient = False: self._get_per_token_logps(
                model, input_ids, attention_mask, logits_to_keep, compute_efficient
            )
            if hasattr(self, "_get_per_token_logps")
            else self._get_per_token_logps_and_entropies(
                model,
                input_ids,
                attention_mask,
                logits_to_keep,
                batch_size,
                compute_entropy,
                compute_efficient,
            )[0]
        )  # logps

        per_token_logps = get_logps_func(
            model, input_ids, attention_mask, logits_to_keep, compute_efficient = True
        )
        # Compute the KL divergence between the model and the reference model
        # _prepare_inputs doesn't return reference log probs anymore. We need to calculate it ourselves.
        # https://github.com/huggingface/trl/blob/05bc43e960396581e458195b8388efe6b82cae1f/trl/trainer/grpo_trainer.py#L1328
        # if self.beta != 0.0:
        #     with torch.inference_mode(), model.disable_adapter():
        #         ref_per_token_logps = per_token_logps = get_logps_func(model, input_ids, attention_mask, logits_to_keep)
        # else:
        #     ref_per_token_logps = None
        ref_logps = inputs.get("ref_per_token_logps", None)
        # per_token_kl = torch.exp(ref_per_token_logps - per_token_logps) - (ref_per_token_logps - per_token_logps) - 1
        # x - x.detach() allows for preserving gradients from x
        advantages = inputs["advantages"]
        # per_token_loss = torch.exp(per_token_logps - per_token_logps.detach()) * advantages.unsqueeze(1)
        # per_token_loss = -(per_token_loss - self.beta * per_token_kl)
        # loss = ((per_token_loss * completion_mask).sum(dim=1) / completion_mask.sum(dim=1)).mean()
        old_logps = inputs.get("old_per_token_logps", None)

        input_ids = input_ids[:, -logits_to_keep:]

        # Get logit softcapping and logit scale
        logit_softcapping = _unsloth_get_final_logit_softcapping(model.config)  # Gemma
        logit_scale_multiply = getattr(model.config, "logit_scale", 0)  # Cohere
        if logit_scale_multiply is None:
            logit_scale_multiply = 0
        logit_scale_divide = getattr(model.config, "logits_scaling", 0)  # Granite
        if logit_scale_divide is None:
            logit_scale_divide = 0

        max_left_pad = inputs.get("max_left_pad", 0)
        if per_token_logps is not None:
            loss_mask = completion_mask
            if tool_mask is not None:
                if tool_mask.shape != completion_mask.shape:
                    raise ValueError(
                        "tool_mask/env_mask must have the same shape as completion_mask"
                    )
                loss_mask = completion_mask * tool_mask.to(
                    device = completion_mask.device,
                    dtype = completion_mask.dtype,
                )
            (
                loss,
                completion_length,
                mean_kl,
                delta,
                flat_is_ratio,
                coef_1,
                completion_mask,
            ) = grpo_compute_loss_slow(
                ref_logps,
                per_token_logps,
                old_logps,
                sampling_per_token_logps,
                input_ids,
                loss_mask,
                self.beta,
                advantages,
                pixel_values = pixel_values,
                image_grid_thw = image_grid_thw,
                loss_type = self.args.loss_type,
                importance_sampling_level = self.importance_sampling_level,
                epsilon_low = self.epsilon_low,
                epsilon_high = self.epsilon_high,
                max_completion_length = self.args.max_completion_length,
                delta = self.args.delta,
                temperature = self.args.temperature,
                max_left_pad = max_left_pad,
                logit_softcapping = logit_softcapping,
                logit_scale_multiply = logit_scale_multiply,
                logit_scale_divide = logit_scale_divide,
                num_items_in_batch = num_items_in_batch,
                current_gradient_accumulation_steps = current_gradient_accumulation_steps,
                num_processes = num_processes,
            )
        else:

            def _unsloth_requires_multi_image_zoo(value):
                if value is None:
                    return False
                if isinstance(value, torch.Tensor):
                    counts = value.detach().cpu().reshape(-1).tolist()
                else:
                    counts = list(value)
                return any(int(n) != 1 for n in counts)

            if _unsloth_requires_multi_image_zoo(num_images) and not getattr(
                self, "_unsloth_grpo_zoo_checked", False
            ):
                _supports_num_images = (
                    "num_images" in inspect.signature(grpo_accumulated_loss).parameters
                )
                if not _supports_num_images:
                    try:
                        _zoo_src = inspect.getsource(grpo_accumulated_loss)
                    except (TypeError, OSError):
                        _zoo_src = ""
                    _supports_num_images = "num_images" in _zoo_src
                if not _supports_num_images:
                    raise RuntimeError(
                        "Multi-image GRPO requires an unsloth_zoo build whose "
                        "grpo_accumulated_loss handles num_images. Please upgrade "
                        "unsloth_zoo (see https://github.com/unslothai/unsloth-zoo/pull/613)."
                    )
                self._unsloth_grpo_zoo_checked = True
            if tool_mask is not None and not getattr(
                self, "_unsloth_grpo_tool_mask_zoo_checked", False
            ):
                _supports_tool_mask = (
                    "tool_mask" in inspect.signature(grpo_accumulated_loss).parameters
                )
                if not _supports_tool_mask:
                    try:
                        _zoo_src = inspect.getsource(grpo_accumulated_loss)
                    except (TypeError, OSError):
                        _zoo_src = ""
                    _supports_tool_mask = "tool_mask" in _zoo_src
                if not _supports_tool_mask:
                    raise RuntimeError(
                        "env_mask/tool_mask GRPO requires an unsloth_zoo build whose "
                        "grpo_accumulated_loss handles tool_mask. Please upgrade "
                        "unsloth_zoo."
                    )
                self._unsloth_grpo_tool_mask_zoo_checked = True
            _grpo_accumulated_loss_kwargs = {}
            if tool_mask is not None:
                _grpo_accumulated_loss_kwargs["tool_mask"] = tool_mask
            if hasattr(self.args, "loss_type"):
                (
                    loss,
                    completion_length,
                    mean_kl,
                    delta,
                    flat_is_ratio,
                    coef_1,
                    completion_mask,
                ) = grpo_accumulated_loss(
                    trainer = self,
                    input_ids = _input_ids,
                    pixel_values = pixel_values,
                    image_grid_thw = image_grid_thw,
                    pixel_attention_mask = pixel_attention_mask,
                    image_sizes = image_sizes,
                    num_images = num_images,
                    logits_to_keep = logits_to_keep,
                    completion_mask = completion_mask,
                    advantages = advantages,
                    old_logps = old_logps,
                    ref_logps = ref_logps,
                    n_chunks = self.args.unsloth_num_chunks,
                    loss_type = self.args.loss_type,
                    importance_sampling_level = self.importance_sampling_level,
                    epsilon_low = self.epsilon_low,
                    epsilon_high = self.epsilon_high,
                    max_completion_length = self.args.max_completion_length,
                    delta = self.args.delta,
                    temperature = self.args.temperature,
                    max_left_pad = max_left_pad,
                    logit_softcapping = logit_softcapping,
                    logit_scale_multiply = logit_scale_multiply,
                    logit_scale_divide = logit_scale_divide,
                    attention_mask = attention_mask,
                    num_items_in_batch = num_items_in_batch,
                    current_gradient_accumulation_steps = current_gradient_accumulation_steps,
                    num_processes = num_processes,
                    sampling_per_token_logps = sampling_per_token_logps,
                    token_type_ids = token_type_ids,
                    mm_token_type_ids = mm_token_type_ids,
                    **_grpo_accumulated_loss_kwargs,
                )
            else:
                # to ensure backwards compatibility with trl 0.15.2 and maybe even 0.17
                loss, completion_length, mean_kl, coef_1, completion_mask = grpo_accumulated_loss(
                    trainer = self,
                    input_ids = _input_ids,
                    pixel_values = pixel_values,
                    image_grid_thw = image_grid_thw,
                    pixel_attention_mask = pixel_attention_mask,
                    image_sizes = image_sizes,
                    num_images = num_images,
                    logits_to_keep = logits_to_keep,
                    completion_mask = completion_mask,
                    advantages = advantages,
                    old_logps = old_logps,
                    ref_logps = ref_logps,
                    n_chunks = self.args.unsloth_num_chunks,
                    temperature = self.args.temperature,
                    logit_softcapping = logit_softcapping,
                    logit_scale_multiply = logit_scale_multiply,
                    logit_scale_divide = logit_scale_divide,
                    attention_mask = attention_mask,
                    token_type_ids = token_type_ids,
                    mm_token_type_ids = mm_token_type_ids,
                    **_grpo_accumulated_loss_kwargs,
                )
        if "train" in self._metrics:
            mode = "eval" if self.control.should_evaluate else "train"
            self._metrics[mode]["completion_length"].append(completion_length.item())
            self._metrics[mode]["kl"].append(mean_kl.item())
        else:
            self._metrics["completion_length"].append(completion_length.item())
            self._metrics["kl"].append(mean_kl.item())

        if (
            self.use_vllm
            and delta is not None
            and getattr(self, "vllm_importance_sampling_correction", False)
        ):
            mean_delta = (
                torch.mean(delta)
                if delta.numel() > 0
                else torch.tensor(0.0, device = self.model.device)
            )
            max_delta = (
                torch.max(delta)
                if delta.numel() > 0
                else torch.tensor(0.0, device = self.model.device)
            )
            self._metrics[mode]["sampling/sampling_logp_difference/mean"].append(
                self.accelerator.gather(mean_delta).mean().item()
            )
            self._metrics[mode]["sampling/sampling_logp_difference/max"].append(
                self.accelerator.gather(max_delta).max().item()
            )

            min_importance_sampling_ratio = (
                torch.min(flat_is_ratio)
                if flat_is_ratio.numel() > 0
                else torch.tensor(0.0, device = self.model.device)
            )
            mean_importance_sampling_ratio = (
                torch.mean(flat_is_ratio)
                if flat_is_ratio.numel() > 0
                else torch.tensor(0.0, device = self.model.device)
            )
            max_importance_sampling_ratio = (
                torch.max(flat_is_ratio)
                if flat_is_ratio.numel() > 0
                else torch.tensor(0.0, device = self.model.device)
            )
            self._metrics[mode]["sampling/importance_sampling_ratio/min"].append(
                self.accelerator.gather(min_importance_sampling_ratio)
                .nan_to_num(nan = float("inf"))
                .min()
                .item()
            )
            self._metrics[mode]["sampling/importance_sampling_ratio/mean"].append(
                self.accelerator.gather(mean_importance_sampling_ratio).nanmean().item()
            )
            self._metrics[mode]["sampling/importance_sampling_ratio/max"].append(
                self.accelerator.gather(max_importance_sampling_ratio)
                .nan_to_num(nan = float("-inf"))
                .max()
                .item()
            )

        completion_token_count = completion_mask.sum().clamp(min = 1.0)

        def masked_batch_mean(x):
            if x.shape[1] == 1:  # when importance_sampling_level == "sequence"
                return x.mean()
            else:
                return (x * completion_mask).sum() / completion_token_count

        if advantages.dim() == 1:
            advantages = advantages.unsqueeze(1)

        if self.loss_type in ["grpo", "bnpo", "dr_grpo", "dapo"]:
            # Compute the clipped probability ratios
            is_low_clipped = (coef_1 < 1 - self.epsilon_low) & (advantages < 0)
            is_high_clipped = (coef_1 > 1 + self.epsilon_high) & (advantages > 0)
            is_region_clipped = is_low_clipped | is_high_clipped

            low_clip = masked_batch_mean(is_low_clipped.float())
            high_clip = masked_batch_mean(is_high_clipped.float())
            clip_ratio = masked_batch_mean(is_region_clipped.float())

            gathered_low_clip = self.accelerator.gather(low_clip)
            self._metrics[mode]["clip_ratio/low_mean"].append(gathered_low_clip.nanmean().item())
            self._metrics[mode]["clip_ratio/low_min"].append(nanmin(gathered_low_clip).item())
            gathered_high_clip = self.accelerator.gather(high_clip)
            self._metrics[mode]["clip_ratio/high_mean"].append(gathered_high_clip.nanmean().item())
            self._metrics[mode]["clip_ratio/high_max"].append(nanmax(gathered_high_clip).item())
            gathered_clip_ratio = self.accelerator.gather(clip_ratio)
            self._metrics[mode]["clip_ratio/region_mean"].append(
                gathered_clip_ratio.nanmean().item()
            )
        elif self.loss_type == "cispo":
            is_cispo_clipped = (coef_1 > self.epsilon_high) & (advantages > 0)
            cispo_clip_ratio = masked_batch_mean(is_cispo_clipped.float())
            gathered_cispo_clip_ratio = self.accelerator.gather(cispo_clip_ratio)
            self._metrics[mode]["cispo_clip_ratio"].append(
                gathered_cispo_clip_ratio.nanmean().item()
            )

        return loss

    @staticmethod
    def get_off_policy_mask(
        advantages: torch.Tensor,
        per_token_logps: torch.Tensor,
        sampling_per_token_logps: torch.Tensor,
        mask: torch.Tensor,
        off_policy_threshold: float,
    ) -> torch.Tensor:
        """
        Computes the Off-Policy Sequence Mask from DeepSeek-V3.2 paper. Returns a (B, 1) tensor where 1.0 indicates
        "Keep" and 0.0 indicates "Drop".
        """
        # forward KL div: log(pi_old) - log(pi_theta)
        kl_div = sampling_per_token_logps - per_token_logps.detach()
        # Sequence-level Mean KL (ignoring prompt+padding)
        seq_kl_sum = (kl_div * mask).sum(dim=1, keepdim=True)
        avg_seq_kl = seq_kl_sum / mask.sum(dim=1, keepdim=True).clamp(min=1.0)
        # Keep if (Advantage >= 0) OR (KL <= delta)
        is_pos_adv = advantages >= 0
        is_low_kl = avg_seq_kl <= off_policy_threshold
        return (is_pos_adv | is_low_kl).to(dtype=mask.dtype)  # (B, 1)

    @staticmethod
    @torch.no_grad()
    def get_gamma_weights(
        advantages: torch.Tensor,
        log_ratio_per_token: torch.Tensor,
        mask: torch.Tensor,
        importance_sampling_ratio: torch.Tensor | None,  # (B, T)
        k_pos: float = 2.0,
        lambda_pos: float = 3.0,
        k_neg: float = 3.0,
        lambda_neg: float = 2.0,
    ) -> torch.Tensor:
        """
        Computes the Gamma weights for the VESPO loss. For reference:
            φ(w) = e^λ × w^k × e^{-λw} is the gamma weighting (normalized so φ(1)=1)
                with w = sequence-level importance sampling ratio
        note: we will compute φ(w) in log space

        φ(w) is detached via @torch.no_grad(), only acts as gradient scaling coefficient

        VESPO loss = -φ(w) × A × log_prob, gradient naturally gives φ(w) × A × ∇log π
        """
        # reducing clamp range directly to log(1e-8) ~ -18.42, to avoid recomputing log_w=log(w.clamp(min=1e-8)) later
        # This is solely for matching truthfully the original implementation, otherwise keeping -20 could be fine.
        lower_clamp = math.log(1e-8)

        # Sequence-level log ratio Σ log(π_θ/π_old) (not a mean like for `log_importance_weights`)
        log_ratio_clamped = torch.clamp(log_ratio_per_token, -20.0, 20.0)
        seq_log_ratio = torch.sum(log_ratio_clamped * mask, dim=-1, keepdim=True)  # (B, 1)

        # Apply token-level TIS or MIS correction (in log space)
        if importance_sampling_ratio is not None:
            log_is_ratio = torch.clamp(torch.log(importance_sampling_ratio), lower_clamp, 20.0)
            # log(w) = log(π_θ/π_old) + log(π_old/π_sampler)
            seq_log_ratio += torch.sum(log_is_ratio, dim=-1, keepdim=True)

        log_w_seq = torch.clamp(seq_log_ratio, lower_clamp, 20.0)
        w_seq = torch.exp(log_w_seq)

        # compute k and lambda based on advantage sign
        is_nonneg_adv = advantages >= 0
        k_seq = torch.where(is_nonneg_adv, k_pos, k_neg)
        lambda_seq = torch.where(is_nonneg_adv, lambda_pos, lambda_neg).clamp(min=1e-4)

        # log(φ(w)) = λ + k × log(w) - λ × w
        log_phi = lambda_seq + k_seq * log_w_seq - lambda_seq * w_seq
        phi_seq = torch.exp(log_phi).nan_to_num(nan=0.0, posinf=0.0, neginf=0.0)

        return phi_seq  # (B, 1)

    def _compute_loss(self, model, inputs):
        # Compute the per-token log probabilities for the model
        prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"]
        completion_ids, completion_mask = inputs["completion_ids"], inputs["completion_mask"]
        input_ids = torch.cat([prompt_ids, completion_ids], dim=1)
        attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)
        logits_to_keep = completion_ids.size(1)  # we only need to compute the logits for the completion tokens
        mask = completion_mask if "tool_mask" not in inputs else completion_mask * inputs["tool_mask"]

        # Compute the per_token_logps and the entropy at each position in the completion
        per_token_logps, entropies, aux_loss = self._get_per_token_logps_and_entropies(
            model,
            input_ids,
            attention_mask,
            logits_to_keep,
            compute_entropy=True,
            compute_aux_loss=self.aux_loss_enabled,
            pixel_values=inputs.get("pixel_values"),
            image_grid_thw=inputs.get("image_grid_thw"),
            num_images=inputs.get("num_images"),
            pixel_attention_mask=inputs.get("pixel_attention_mask"),
            spatial_shapes=inputs.get("spatial_shapes"),
            num_tiles=inputs.get("num_tiles"),
            image_sizes=inputs.get("image_sizes"),
            token_type_ids=inputs.get("token_type_ids"),
            mm_token_type_ids=inputs.get("mm_token_type_ids"),
            image_position_ids=inputs.get("image_position_ids"),
        )

        if self.top_entropy_quantile < 1.0:
            entropy_mask = self.get_high_entropy_mask(entropies, mask, 1 - self.top_entropy_quantile)
        else:
            entropy_mask = None

        # Compute the loss
        advantages = inputs["advantages"]
        # In the base GRPO implementation, advantages are expected to have shape (B,). To support subclasses that
        # provide advantages with shape (B, T) (e.g., MiniLLM), we *conditionally* unsqueeze the tensor.
        if advantages.dim() == 1:
            advantages = advantages.unsqueeze(1)
        # When num_iterations == 1 and steps_per_generation <= gradient_accumulation_steps,
        # old_per_token_logps == per_token_logps. In this case we can skip its computation
        # (see _generate_and_score_completions) and instead use per_token_logps.detach().
        # The exception is when using vLLM, where we always compute old_per_token_logps
        # for importance sampling
        old_per_token_logps = inputs.get("old_per_token_logps")
        old_per_token_logps = per_token_logps.detach() if old_per_token_logps is None else old_per_token_logps

        if self.off_policy_mask_threshold is not None:
            # OPSM should use inference-time logprobs to detect both sources of off-policyness:
            # 1. Drift from gradient updates (always present)
            # 2. Drift from training-inference mismatch (when using vLLM)
            # When using vLLM, prioritize sampling_per_token_logps, otherwise use old_per_token_logps
            sampling_per_token_logps = inputs.get("sampling_per_token_logps", old_per_token_logps)

            off_policy_mask = self.get_off_policy_mask(
                advantages=advantages,
                per_token_logps=per_token_logps,
                sampling_per_token_logps=sampling_per_token_logps,
                mask=mask,
                off_policy_threshold=self.off_policy_mask_threshold,
            )

        log_ratio = per_token_logps - old_per_token_logps
        if self.importance_sampling_level == "token":
            log_importance_weights = log_ratio
        elif self.importance_sampling_level == "sequence":
            log_importance_weights = (log_ratio * mask).sum(-1) / mask.sum(-1).clamp(min=1.0)
            log_importance_weights = log_importance_weights.unsqueeze(-1)
        else:
            raise ValueError(
                f"Unknown importance sampling level: {self.importance_sampling_level}. Possible values are 'token' "
                "and 'sequence'."
            )

        coef_1 = torch.exp(log_importance_weights)

        # Compute the KL divergence between the model and the reference model
        if self.beta != 0.0:
            ref_per_token_logps = inputs["ref_per_token_logps"]
            per_token_kl = (
                torch.exp(ref_per_token_logps - per_token_logps) - (ref_per_token_logps - per_token_logps) - 1
            )
            # Importance sampling correction for the KL divergence
            if self.args.use_bias_correction_kl:
                per_token_kl = per_token_kl * coef_1

        # From here, log_importance_weights (and all subsequent tensors, coef_1, coef_2, etc.) shape depends on
        # importance_sampling_level: "token" level: (B, T); "sequence" level: (B, 1)
        if self.loss_type == "cispo":
            clamped_ratios = torch.clamp(coef_1, max=self.epsilon_high).detach()
            per_token_loss = -clamped_ratios * advantages * per_token_logps
        elif self.loss_type in ["grpo", "bnpo", "dr_grpo", "dapo", "luspo"]:
            coef_2 = torch.clamp(coef_1, 1 - self.epsilon_low, 1 + self.epsilon_high)
            # Two-sided clipping
            if self.args.delta is not None:
                coef_1 = torch.clamp(coef_1, max=self.args.delta)

            per_token_loss1 = coef_1 * advantages
            per_token_loss2 = coef_2 * advantages
            per_token_loss = -torch.min(per_token_loss1, per_token_loss2)
        elif self.loss_type == "sapo":
            temperatures = torch.where(advantages > 0, self.args.sapo_temperature_pos, self.args.sapo_temperature_neg)
            soft_coef_1 = torch.sigmoid(temperatures * (coef_1 - 1)) * 4 / temperatures
            per_token_loss = -soft_coef_1 * advantages
        elif self.loss_type == "vespo":
            phi_seq = self.get_gamma_weights(
                advantages=advantages,
                log_ratio_per_token=log_ratio,
                mask=mask,
                importance_sampling_ratio=inputs.get("importance_sampling_ratio"),
                k_pos=self.args.vespo_k_pos,
                lambda_pos=self.args.vespo_lambda_pos,
                k_neg=self.args.vespo_k_neg,
                lambda_neg=self.args.vespo_lambda_neg,
            )
            per_token_loss = -phi_seq * advantages * per_token_logps
        else:
            raise ValueError(f"Unknown loss type: {self.loss_type}")

        if self.off_policy_mask_threshold is not None:
            per_token_loss = per_token_loss * off_policy_mask

        if entropy_mask is not None:
            per_token_loss = per_token_loss * entropy_mask

        if self.use_vllm and self.vllm_importance_sampling_correction and self.loss_type != "vespo":
            per_token_loss = per_token_loss * inputs["importance_sampling_ratio"]

        if self.beta != 0.0:
            per_token_loss = per_token_loss + self.beta * per_token_kl

        mode = "train" if self.model.training else "eval"
        if self.loss_type in ["grpo", "sapo"]:
            loss = ((per_token_loss * mask).sum(-1) / mask.sum(-1).clamp(min=1.0)).mean()
            normalizer = self.current_gradient_accumulation_steps if mode == "train" else 1.0  # no accum in eval
            loss = loss / normalizer
        elif self.loss_type == "bnpo":
            loss = (per_token_loss * mask).sum() / mask.sum().clamp(min=1.0)
            normalizer = self.current_gradient_accumulation_steps if mode == "train" else 1.0  # no accum in eval
            loss = loss / normalizer
        elif self.loss_type == "dr_grpo":
            loss = (per_token_loss * mask).sum() / (per_token_loss.size(0) * self.max_completion_length)
            normalizer = self.current_gradient_accumulation_steps if mode == "train" else 1.0  # no accum in eval
            loss = loss / normalizer
        elif self.loss_type in ["cispo", "dapo", "vespo"]:
            normalizer = inputs["num_items_in_batch"] / self.accelerator.num_processes
            loss = (per_token_loss * mask).sum() / normalizer
        elif self.loss_type == "luspo":
            # Unless importance_sampling_level="token" (not recommended here), per_token_loss is expected to be (B, 1)
            loss = (per_token_loss * mask.sum(1, keepdim=True)).mean()
            normalizer = self.current_gradient_accumulation_steps if mode == "train" else 1.0
            loss = loss / normalizer
        else:
            raise ValueError(f"Unknown loss type: {self.loss_type}")

        # The policy loss above is scaled for gradient accumulation (HF auto-scaling is off here), so scale aux too
        if self.aux_loss_enabled:
            normalizer = self.current_gradient_accumulation_steps if mode == "train" else 1.0
            loss = loss + self.router_aux_loss_coef * aux_loss / normalizer
            self._metrics[mode]["aux_loss"].append(self.accelerator.gather_for_metrics(aux_loss).mean().item())

        # Log the metrics
        completion_token_count = mask.sum().clamp(min=1.0)

        def masked_batch_mean(x):
            if x.shape[1] == 1:  # when importance_sampling_level == "sequence"
                return x.mean()
            else:
                return (x * mask).sum() / completion_token_count

        if self.beta != 0.0:
            mean_kl = masked_batch_mean(per_token_kl)
            self._metrics[mode]["kl"].append(self.accelerator.gather(mean_kl).nanmean().item())

        mean_entropy = masked_batch_mean(entropies)
        self._metrics[mode]["entropy"].append(self.accelerator.gather(mean_entropy).nanmean().item())

        if self.loss_type in ["grpo", "bnpo", "dr_grpo", "dapo", "luspo"]:
            # Compute the clipped probability ratios
            is_low_clipped = (coef_1 < 1 - self.epsilon_low) & (advantages < 0)
            is_high_clipped = (coef_1 > 1 + self.epsilon_high) & (advantages > 0)
            is_region_clipped = is_low_clipped | is_high_clipped

            low_clip = masked_batch_mean(is_low_clipped.float())
            high_clip = masked_batch_mean(is_high_clipped.float())
            clip_ratio = masked_batch_mean(is_region_clipped.float())

            gathered_low_clip = self.accelerator.gather(low_clip)
            self._metrics[mode]["clip_ratio/low_mean"].append(gathered_low_clip.nanmean().item())
            self._metrics[mode]["clip_ratio/low_min"].append(nanmin(gathered_low_clip).item())
            gathered_high_clip = self.accelerator.gather(high_clip)
            self._metrics[mode]["clip_ratio/high_mean"].append(gathered_high_clip.nanmean().item())
            self._metrics[mode]["clip_ratio/high_max"].append(nanmax(gathered_high_clip).item())
            gathered_clip_ratio = self.accelerator.gather(clip_ratio)
            self._metrics[mode]["clip_ratio/region_mean"].append(gathered_clip_ratio.nanmean().item())
        elif self.loss_type == "cispo":
            is_cispo_clipped = (coef_1 > self.epsilon_high) & (advantages > 0)
            cispo_clip_ratio = masked_batch_mean(is_cispo_clipped.float())
            gathered_cispo_clip_ratio = self.accelerator.gather(cispo_clip_ratio)
            self._metrics[mode]["cispo_clip_ratio"].append(gathered_cispo_clip_ratio.nanmean().item())
        elif self.loss_type == "vespo":
            gathered_phi_seq = self.accelerator.gather(phi_seq)
            self._metrics[mode]["vespo/phi_seq_mean"].append(gathered_phi_seq.nanmean().item())

        return loss

    # During eval, Trainer calls prediction_step. If no labels are present in the inputs, it only runs forward and
    # returns logits. We override prediction_step to force compute_loss, because this trainer doesn't involve labels.
    def prediction_step(self, model, inputs, prediction_loss_only, ignore_keys: list[str] | None = None):
        inputs = self._prepare_inputs(inputs)
        with torch.no_grad():
            with self.compute_loss_context_manager():
                loss = self.compute_loss(model, inputs)
            loss = loss.mean().detach()
        return loss, None, None

    def log(self, logs: dict[str, float], start_time: float | None = None) -> None:
        mode = "train" if self.model.training else "eval"
        # Average the metrics
        metrics = {}
        for key, val in self._metrics[mode].items():
            # Filter out NaN values before averaging. A reward function that returns None for all samples
            # in a batch produces NaN for that batch's metric. With logging_steps > 1, a naive sum()/len()
            # would let a single NaN contaminate valid data from other batches. Only return None when no
            # valid values remain (e.g. JSON loggers crash on float NaN).
            valid = [v for v in val if not math.isnan(v)]
            metrics[key] = sum(valid) / len(valid) if valid else None

        # This method can be called both in training and evaluation. When called in evaluation, the keys in `logs`
        # start with "eval_". We need to add the prefix "eval_" to the keys in `metrics` to match the format.
        if mode == "eval":
            metrics = {f"eval_{key}": val for key, val in metrics.items()}

        logs.update(metrics)
        super().log(logs, start_time)
        self._metrics[mode].clear()

        if self.accelerator.is_main_process and self.log_completions:
            if is_rich_available():
                print_prompt_completions_sample(
                    self._logs["prompt"],
                    self._logs["completion"],
                    self._logs["rewards"],
                    self._logs["advantages"],
                    self.state.global_step,
                    self.num_completions_to_print,
                    extra=dict(self._logs["extra"]),
                )

            logging_backends = []
            if self.args.report_to and "wandb" in self.args.report_to and wandb.run is not None:
                logging_backends.append(wandb)
            if self.args.report_to and "trackio" in self.args.report_to:
                logging_backends.append(trackio)

            table = {
                "step": [self.state.global_step] * len(self._logs["prompt"]),
                "prompt": self._logs["prompt"],
                "completion": self._logs["completion"],
                **self._logs["rewards"],
                **self._logs["extra"],
                "advantage": self._logs["advantages"],
            }

            df_base = pd.DataFrame(table)
            df_base.to_parquet(
                os.path.join(
                    self.args.output_dir,
                    "completions",
                    f"completions_{self.state.global_step:05d}.parquet",
                )
            )

            images_raw = self._logs["images"] or []

            for logging_backend in logging_backends:
                if images_raw:
                    images = []
                    for image_list in self._logs["images"]:
                        if image_list:
                            images.append([logging_backend.Image(image) for image in image_list])
                        else:
                            images.append([])
                    df = pd.concat(
                        [df_base, pd.Series(images, name="image")],
                        axis=1,
                        copy=False,
                    )
                else:
                    df = df_base

                if self.log_unique_prompts:
                    df = df.drop_duplicates(subset=["prompt"])

                logging_backend.log({"completions": logging_backend.Table(dataframe=df)})

    # Ensure the model card is saved along with the checkpoint
    def _save_checkpoint(self, model, trial):
        if self.args.hub_model_id is None:
            model_name = Path(self.args.output_dir).name
        else:
            model_name = self.args.hub_model_id.split("/")[-1]
        self.create_model_card(model_name=model_name)
        super()._save_checkpoint(model, trial)
class UnslothGRPOTrainer(_UnslothGRPOTrainer):
    """
    
    Trainer for the Group Relative Policy Optimization (GRPO) method. This algorithm was initially proposed in the
    paper [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language
    Models](https://huggingface.co/papers/2402.03300).

    Example:

    ```python
    >>> from trl import GRPOTrainer
    >>> from trl.rewards import accuracy_reward
    >>> from datasets import load_dataset

    >>> dataset = load_dataset("trl-lib/DeepMath-103K", split="train")

    >>> trainer = GRPOTrainer(
    ...     model="Qwen/Qwen2.5-0.5B-Instruct",
    ...     reward_funcs=accuracy_reward,
    ...     train_dataset=dataset,
    ... )
    >>> trainer.train()
    ```

    Args:
        model (`str` or [`~transformers.PreTrainedModel`] or [`~peft.PeftModel`]):
            Model to be trained. Can be either:

            - A string, being the *model id* of a pretrained model hosted inside a model repo on huggingface.co, or a
              path to a *directory* containing model weights saved using
              [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded
              using `<ModelArchitecture>.from_pretrained` (where `<ModelArchitecture>` is derived from the model
              config) with the keyword arguments in `args.model_init_kwargs`.
            - A [`~transformers.PreTrainedModel`] object. Only causal language models are supported.
            - A [`~peft.PeftModel`] object. Only causal language models are supported.
        reward_funcs (`RewardFunc | list[RewardFunc]`):
            Reward functions to be used for computing the rewards. To compute the rewards, we call all the reward
            functions with the prompts and completions and sum the rewards. Can be either:

            - A single reward function, such as:
                - A string: The *model ID* of a pretrained model hosted inside a model repo on huggingface.co, or a
                path to a *directory* containing model weights saved using
                [`~transformers.PreTrainedModel.save_pretrained`], e.g., `'./my_model_directory/'`. The model is loaded
                using [`~transformers.AutoModelForSequenceClassification.from_pretrained`] with `num_labels=1` and the
                keyword arguments in `args.model_init_kwargs`.
                - A [`~transformers.PreTrainedModel`] object: Only sequence classification models are supported.
                - A custom reward function: The function is provided with the prompts and the generated completions,
                  plus any additional columns in the dataset. It should return a list of rewards. Custom reward
                   functions can be either synchronous or asynchronous and can also return `None` when the reward is
                   not applicable to those samples. This is useful for multi-task training where different reward
                   functions apply to different types of samples. When a reward function returns `None` for a sample,
                   that reward function is excluded from the reward calculation for that sample. For more details, see
                   [Using a custom reward
                  function](#using-a-custom-reward-function).

                  The trainer's state is also passed to the reward function. The trainer's state is an instance of
                  [`~transformers.TrainerState`] and can be accessed by accessing the `trainer_state` argument to the
                  reward function's signature.
            - A list of reward functions, where each item can independently be any of the above types. Mixing different
            types within the list (e.g., a string model ID and a custom reward function) is allowed.
        args ([`GRPOConfig`], *optional*):
            Configuration for this trainer. If `None`, a default configuration is used.
        train_dataset ([`~datasets.Dataset`] or [`~datasets.IterableDataset`]):
            Dataset to use for training. It must include a column `"prompt"`. Any additional columns in the dataset is
            ignored. The format of the samples can be either:

            - [Standard](dataset_formats#standard): Each sample contains plain text.
            - [Conversational](dataset_formats#conversational): Each sample contains structured messages (e.g., role
              and content).
        eval_dataset ([`~datasets.Dataset`], [`~datasets.IterableDataset`] or `dict[str, Dataset | IterableDataset]`):
            Dataset to use for evaluation. It must meet the same requirements as `train_dataset`.
        processing_class ([`~transformers.PreTrainedTokenizerBase`], [`~transformers.ProcessorMixin`], *optional*):
            Processing class used to process the data. The padding side must be set to "left". If `None`, the
            processing class is loaded from the model's name with [`~transformers.AutoProcessor.from_pretrained`]. A
            padding token, `tokenizer.pad_token`, must be set. If the processing class has not set a padding token,
            `tokenizer.eos_token` will be used as the default.
        reward_processing_classes ([`~transformers.PreTrainedTokenizerBase`] or `list[PreTrainedTokenizerBase]`, *optional*):
            Processing classes corresponding to the reward functions specified in `reward_funcs`. Can be either:

            - A single processing class: Used when `reward_funcs` contains only one reward function.
            - A list of processing classes: Must match the order and length of the reward functions in `reward_funcs`.
            If set to `None`, or if an element of the list corresponding to a [`~transformers.PreTrainedModel`] is
            `None`, the tokenizer for the model is automatically loaded using
            [`~transformers.AutoTokenizer.from_pretrained`]. For elements in `reward_funcs` that are custom reward
            functions (not [`~transformers.PreTrainedModel`]), the corresponding entries in `reward_processing_classes`
            are ignored.
        callbacks (list of [`~transformers.TrainerCallback`], *optional*):
            List of callbacks to customize the training loop. Will add those to the list of default callbacks detailed
            in [here](https://huggingface.co/docs/transformers/main_classes/callback).

            If you want to remove one of the default callbacks used, use the [`~transformers.Trainer.remove_callback`]
            method.
        optimizers (`tuple[torch.optim.Optimizer | None, torch.optim.lr_scheduler.LambdaLR | None]`, *optional*, defaults to `(None, None)`):
            A tuple containing the optimizer and the scheduler to use. Will default to an instance of `AdamW` on your
            model and a scheduler given by [`~transformers.get_linear_schedule_with_warmup`] controlled by `args`.
        peft_config ([`~peft.PeftConfig`], *optional*):
            PEFT configuration used to wrap the model. If `None`, the model is not wrapped.
        tools (list of `Callable`, *optional*):
            A list of callable tool functions (sync or async) that the model can invoke during generation. Each tool
            should be a standard Python function with properly type-hinted arguments and return values, and a
            Google-style docstring describing its purpose, arguments, and return value. For more details, see:
            https://huggingface.co/docs/transformers/en/chat_extras#passing-tools. The model uses the function's name,
            type hints, and docstring to determine how to call it. Ensure that the model's chat template supports tool
            use and that it has been fine-tuned for tool calling.
        rollout_func (`RolloutFunc`, *optional*):
            Function to use for generating completions. It receives the list of prompts allocated to the current
            process and the trainer instance. It must return a dict with `"prompt_ids"`, `"completion_ids"`, and
            `"logprobs"` fields, and can optionally return `"logprob_token_ids"` (same shape as `"logprobs"`). Any
            other fields are forwarded to the reward functions. The function receives the raw per-process prompt slice
            with no duplication; it is responsible for returning the correct number of completions per prompt (see
            `num_generations` / `num_generations_eval` on the trainer). This feature is experimental and may change or
            be removed at any time without prior notice.
        environment_factory (`EnvironmentFactory`, *optional*):
            A callable that creates and returns an environment instance. The environment class should define methods
            that can be invoked as tools during generation. Each method should comply with the same requirements as the
            `tools` described above. If `environment_factory` is provided, an instance of the environment is created
            for each generation in the batch, allowing for parallel and independent interactions. The environment must
            also implement a callable `reset` method that can be used to reset state between generations. The `reset`
            method should return either `None` or a string: when it returns a string, that string is appended to the
            last user message before generation. This feature is experimental and may change or be removed at any time
            without prior notice.
    
    """
    def __init__(
        self,
        model,
        reward_funcs,
        args = None,
        train_dataset = None,
        eval_dataset = None,
        processing_class = None,
        reward_processing_classes = None,
        callbacks = None,
        peft_config = None,
        tools = None,
        rollout_func = None,
        environment_factory = None,
        **kwargs
    ):
        if args is None: args = UnslothGRPOConfig()
        use_bf16 = getattr(args, 'bf16', False)
        if type(use_bf16) is not bool: use_bf16 = False
        use_fp16 = getattr(args, 'fp16', False)
        if type(use_fp16) is not bool: use_fp16 = False
        force_float32 = False
        full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1'
        if not full_finetuning and (os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1'):
            print('Unsloth: Switching to float32 training since model cannot work with float16')
            force_float32 = True
        mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')
        dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None)
        if dtype is None: dtype = model.get_input_embeddings().weight.dtype
        from unsloth_zoo.utils import _get_dtype
        dtype = _get_dtype(dtype)
        float16 = dtype == torch.float16
        if not force_float32 and (float16 and use_bf16): raise TypeError('Unsloth: Model is in float16 precision but you want to use bfloat16 precision. Set fp16 to `True` and bf16 to `False`')
        if not force_float32 and (not float16 and use_fp16): raise TypeError('Unsloth: Model is in bfloat16 precision but you want to use float16 precision. Set fp16 to `False` and bf16 to `True`')
        if force_float32:
            # Forced float32 training
            args.fp16 = False
            args.bf16 = False
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
            if hasattr(args, 'mixed_precision'): args.mixed_precision = 'no'
            # args.mixed_precision is a new argument which needs to be set now
        elif (not use_bf16 and not use_fp16) and mixed_precision_dtype == 'float32':
            # Mixed precision training
            args.fp16 = float16
            args.bf16 = not float16
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'fp16' if float16 else 'bf16'
            if hasattr(args, 'mixed_precision'): args.mixed_precision = 'fp16' if float16 else 'bf16'
            # args.mixed_precision is a new argument which needs to be set now
        elif mixed_precision_dtype == 'bfloat16':
            # Both False since bfloat16 full finetuning doesn't do any autocasting.
            args.fp16 = False
            args.bf16 = False
            os.environ['ACCELERATE_MIXED_PRECISION'] = 'no'
            if hasattr(args, 'mixed_precision'): args.mixed_precision = 'no'
            # args.mixed_precision is a new argument which needs to be set now
        
        if getattr(args, 'eval_dataset', None) is not None and getattr(args, 'eval_strategy', 'no') == 'no':
            args.eval_strategy = 'steps'
            if getattr(args, 'eval_steps', None) is None: args.eval_steps = 0.1
        ga_steps = getattr(args, 'gradient_accumulation_steps', None)
        if ga_steps is not None and ga_steps > 1:
            from transformers import __version__ as transformers_version
            if Version(transformers_version) <= Version('4.45.2'):
                print('**** Unsloth: Please use our fixed gradient_accumulation_steps by updating transformers, TRL and Unsloth!\n'
                      '`pip install --upgrade --no-cache-dir --force-reinstall --no-deps unsloth transformers trl unsloth_zoo`')
        if getattr(args, 'eval_strategy', 'no') != 'no':
            eval_bsz = getattr(args, 'per_device_eval_batch_size', 8)
            if eval_bsz == 8 and args.per_device_train_batch_size < eval_bsz: args.per_device_eval_batch_size = args.per_device_train_batch_size
            if getattr(args, 'eval_accumulation_steps', None) is None and ga_steps is not None: args.eval_accumulation_steps = ga_steps
        fp16_full_eval = getattr(args, 'fp16_full_eval', False)
        if type(fp16_full_eval) is not bool: fp16_full_eval = False
        bf16_full_eval = getattr(args, 'bf16_full_eval', False)
        if type(bf16_full_eval) is not bool: bf16_full_eval = False
        if args.fp16 and bf16_full_eval: args.bf16_full_eval = False; args.fp16_full_eval = True
        if args.bf16 and fp16_full_eval: args.bf16_full_eval = True; args.fp16_full_eval = False
        if force_float32:
            args.bf16_full_eval = False
            args.fp16_full_eval = False
        elif os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32') == 'bfloat16':
            args.bf16_full_eval = True
            args.fp16_full_eval = False
        elif not bf16_full_eval and not fp16_full_eval:
            args.bf16_full_eval = args.bf16
            args.fp16_full_eval = args.fp16
        _output_logits = False
        if locals().get('compute_metrics', None) is not None: _output_logits = True
        if locals().get('preprocess_logits_for_metrics', None) is not None: _output_logits = True
        if _output_logits:
            os.environ['UNSLOTH_RETURN_LOGITS'] = '1'
        if model is not None:
            _warnings_issued = getattr(model, 'warnings_issued', None)
            if _warnings_issued is None:
                model.warnings_issued = {}
            elif not isinstance(_warnings_issued, dict):
                try:
                    model.warnings_issued = dict(_warnings_issued)
                except Exception:
                    model.warnings_issued = {}
        if 'max_seq_length' not in locals() and not hasattr(args, 'max_seq_length'):
            pass
        else:
            model_max_seq_length = getattr(model, 'max_seq_length', None)
            args_max_seq_length  = getattr(args,  'max_seq_length', None)
            if args_max_seq_length is None and model_max_seq_length is not None:
                max_seq_length = model.max_seq_length
                if hasattr(args, 'max_seq_length'): args.max_seq_length = max_seq_length
            elif args_max_seq_length is not None and model_max_seq_length is not None:
                if args_max_seq_length > model_max_seq_length:
                    print('Unsloth: You set `max_seq_length` as ' + str(args_max_seq_length) + ' but '
                           'the maximum the model supports is ' + str(model_max_seq_length) + '. We shall reduce it.')
                    args.max_seq_length = model_max_seq_length
        if model is not None and hasattr(model, 'for_training'):
            model.for_training(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True))
        if 'tokenizer' in locals() and hasattr(tokenizer, 'padding_side'): tokenizer.padding_side = 'right'
        if 'processing_class' in locals():
            if hasattr(processing_class, 'padding_side'): processing_class.padding_side = 'right'
            if hasattr(processing_class, 'tokenizer') and hasattr(processing_class.tokenizer, 'padding_side'): processing_class.tokenizer.padding_side = 'right'
        other_metrics = []
        if not isinstance(reward_funcs, list): _reward_funcs = [reward_funcs]
        else: _reward_funcs = reward_funcs
        for reward_func in _reward_funcs:
            try:
                reward_func_name = reward_func.__name__
                if True:
                    other_metrics.append(f'rewards/{reward_func_name}/mean')
                if True:
                    other_metrics.append(f'rewards/{reward_func_name}/std')
                if False:
                    other_metrics.append(f'rewards/{reward_func_name}')
            except: pass
        
        from unsloth_zoo.logging_utils import PatchRLStatistics
        PatchRLStatistics('grpo_trainer', other_metrics)
        
        # [TODO] Fix up DataParallel multiplying batch sizes
        # [TODO] DDP works, but DP seems to not work? [TODO]
        if getattr(args, "parallel_mode", None) == ParallelMode.NOT_DISTRIBUTED and args.n_gpu > 1:
            if getattr(args, "_n_gpu", 1) != 1:
                args._n_gpu = 1
        if "model" in locals() and hasattr(model, "for_training"):
            model.for_training(use_gradient_checkpointing=getattr(args, 'gradient_checkpointing', True))
        super().__init__(
            model = model,
            reward_funcs = reward_funcs,
            args = args,
            train_dataset = train_dataset,
            eval_dataset = eval_dataset,
            processing_class = processing_class,
            reward_processing_classes = reward_processing_classes,
            callbacks = callbacks,
            peft_config = peft_config,
            tools = tools,
            rollout_func = rollout_func,
            environment_factory = environment_factory,**kwargs)
        if "model" in locals() and hasattr(model, "for_inference"):
            model.for_inference()
        if hasattr(self, 'neftune_hook_handle'):
            self.neftune_hook_handle.remove()
            if hasattr(self, 'neftune_hook_handle'): del self.neftune_hook_handle
        if getattr(args, 'neftune_noise_alpha', None) is not None:
            model.get_input_embeddings().neftune_noise_alpha = self.neftune_noise_alpha
        pass
        if hasattr(self, 'accelerator'):
            scaler = self.accelerator.scaler
            current_model = model
            while hasattr(current_model, 'model'):
                current_model.accelerator_scaler = scaler
                current_model = current_model.model
            current_model.accelerator_scaler = scaler
        pass
        if hasattr(self, 'train'):
            self.train = MethodType(prepare_for_training_mode(self.__class__.train), self)
        pass
        if hasattr(self, 'llm') and self.llm is not None and hasattr(self.llm, 'get_tokenizer'):
            _vllm_tok = self.llm.get_tokenizer()
            _pc = getattr(self, 'processing_class', None) or getattr(self, 'tokenizer', None)
            if _vllm_tok is not None and _pc is not None and getattr(_pc, 'chat_template', None) is not None and getattr(_vllm_tok, 'chat_template', None) is None:
                _vllm_tok.chat_template = _pc.chat_template
        pass
        
pass


if hasattr(logger, "addFilter"):
    import logging
    class HideLoggingMessage(logging.Filter):
        def __init__(self, text): self.text = text
        def filter(self, x): return not (self.text in x.getMessage())
    pass
    logger.addFilter(HideLoggingMessage("`use_cache=True`"))