unslothai--unsloth/unsloth/models/llama.py

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

import torch
import gc
import math
import functools
from typing import Optional, Tuple, List, Union

from ._utils import *
from ._utils import apply_unsloth_gradient_checkpointing
from ._utils import __version__, importlib_version
from ._utils import move_to_device
from ._utils import (
    _get_inference_mode_context_manager,
    _prepare_model_for_qat,
    is_bfloat16_supported,
    get_quant_type,
)
from .loader_utils import (
    _exclude_rope_inv_freq_from_ddp,
    _get_fp8_mode_and_check_settings,
    _restore_dropped_fp8_scales,
)
from ..utils.packing import (
    get_packed_info_from_kwargs,
    mask_packed_sequence_boundaries,
)
from ..utils.attention_dispatch import (
    AttentionConfig,
    AttentionContext,
    run_attention,
    SDPA,
    select_attention_backend,
    resolve_prefix_seg_info,
)
from torch.nn.functional import scaled_dot_product_attention
from transformers import __version__ as transformers_version
from unsloth_zoo.utils import Version, _get_dtype
from unsloth_zoo.hf_utils import (
    dtype_from_config,
    add_dtype_kwargs,
    fix_lora_auto_mapping,
)
from unsloth_zoo.peft_utils import SKIP_QUANTIZATION_MODULES
from ..device_type import (
    is_hip,
    get_device_type,
    DEVICE_TYPE,
    DEVICE_TYPE_TORCH,
    DEVICE_COUNT,
    ALLOW_PREQUANTIZED_MODELS,
)

transformers_version = Version(transformers_version)
# Transformers moved rotary embeddings out of all attention layers
IS_ATTENTION_REFACTOR = transformers_version > Version("4.47.1")
try:
    from transformers.modeling_layers import GradientCheckpointingLayer
except:
    GradientCheckpointingLayer = type(None)

from transformers.models.llama.modeling_llama import (
    logger,
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
)
from transformers.modeling_attn_mask_utils import (
    _prepare_4d_causal_attention_mask_for_sdpa,
)
from ..kernels import *
from ..tokenizer_utils import *
from .vision import FastBaseModel

# Final patching code
from transformers.models.llama.modeling_llama import (
    LlamaAttention,
    LlamaDecoderLayer,
    LlamaModel,
    LlamaForCausalLM,
)

# For Pytorch 2.1.1
try:
    from transformers.models.llama.modeling_llama import (
        LlamaSdpaAttention,
        LlamaFlashAttention2,
    )
except:
    LlamaSdpaAttention = LlamaAttention
    LlamaFlashAttention2 = LlamaAttention

from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    AutoModelForSequenceClassification,
    BitsAndBytesConfig,
    AutoConfig,
)
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING
from transformers import set_seed as transformers_set_seed
from peft import LoraConfig, TaskType, get_peft_model as _get_peft_model
from peft import PeftModelForCausalLM, PeftModelForSequenceClassification
from ..save import patch_saving_functions
import re, os, inspect, math, sys
import types

try:
    from huggingface_hub.utils import get_token
except:
    # Old HF Hub versions <= 0.0.25
    from huggingface_hub.utils._token import get_token
from triton import __version__ as triton_version

HAS_XFORMERS = xformers is not None
BlockDiagonalCausalMask = xformers.attn_bias.BlockDiagonalCausalMask if HAS_XFORMERS else None

if DEVICE_TYPE == "xpu":
    clean_gpu_cache = torch.xpu.empty_cache
    get_current_device = torch.xpu.current_device
else:
    clean_gpu_cache = torch.cuda.empty_cache
    get_current_device = torch.cuda.current_device


def original_apply_qkv(self, X):
    Q = self.q_proj(X)
    K = self.k_proj(X)
    V = self.v_proj(X)
    return Q, K, V


def original_apply_o(self, X):
    O = self.o_proj(X)
    return O


from math import sqrt as math_sqrt

KV_CACHE_INCREMENT = 512  # KV Cache update size
torch_nn_functional_softmax = torch.nn.functional.softmax
# SDPA has GQA internally
SDPA_HAS_GQA = "enable_gqa" in scaled_dot_product_attention.__doc__

from peft.utils.other import ModulesToSaveWrapper


def _offload_frozen_module_for_training(
    module: ModulesToSaveWrapper,
    device_type: str,
    offload_device: Optional[str] = "cpu",
) -> None:
    """Move the trainable copy to ``device_type`` and offload the frozen original.

    float16 is promoted to float32 for GPU compatibility (e.g. Tesla T4).
    ``offload_device`` currently only supports "cpu"; None leaves the frozen
    module in place. Modifies ``module`` in-place.
    See https://github.com/unslothai/unsloth/pull/1200 (Tesla T4 float32).
    """
    if not hasattr(module, "modules_to_save"):
        return None

    new_dtype = module.modules_to_save.default.weight.dtype
    if new_dtype == torch.float16:
        # See https://github.com/unslothai/unsloth/pull/1200
        # Tesla T4 must use float32 and not float16
        new_dtype = torch.float32

    module.modules_to_save.default.to(device = device_type, dtype = new_dtype, non_blocking = True)
    module.modules_to_save.default.requires_grad_(True)

    # [TODO] Move old module to CPU - should be disk!
    if offload_device is not None:
        module.original_module.to(device = offload_device, non_blocking = True)
    module.original_module.requires_grad_(False)


# Fix new HF's inference code
def _fast_prepare_inputs_for_generation(
    self,
    input_ids,
    attention_mask = None,
    inputs_embeds = None,
    **kwargs,
):
    past_key_values = kwargs.get("past_key_values", None)
    original_attention_mask = attention_mask

    # Only use inputs_embeds on the first step (no cache). Fixes issue #3798.
    use_inputs_embeds = inputs_embeds is not None and past_key_values is None

    if input_ids is not None and input_ids.numel() > 0:
        bs, seq_length = input_ids.shape
        device = input_ids.device
    elif inputs_embeds is not None:
        bs, seq_length, _ = inputs_embeds.shape
        device = inputs_embeds.device
    else:
        bs, seq_length = 1, 0
        device = "cuda" if torch.cuda.is_available() else "cpu"

    if past_key_values is not None:
        # Check for uninitialized DynamicCache
        if len(past_key_values) == 0:
            past_key_values = None
            kwargs["past_key_values"] = None
            use_inputs_embeds = inputs_embeds is not None
        # New since 4.56
        elif hasattr(past_key_values, "get_seq_length") and past_key_values.get_seq_length() == 0:
            past_key_values = None
            kwargs["past_key_values"] = None
            use_inputs_embeds = inputs_embeds is not None
        else:
            if input_ids is not None and input_ids.numel() > 0:
                bs = input_ids.shape[0]
                input_ids = input_ids[:, [-1]]
                device = input_ids.device
                seq_length = 1
            elif inputs_embeds is not None:
                bs, seq_length, _ = inputs_embeds.shape
                device = inputs_embeds.device
            else:
                bs, seq_length = 1, 0
                device = "cuda" if torch.cuda.is_available() else "cpu"

            if hasattr(past_key_values, "get_seq_length"):
                past_len = int(past_key_values.get_seq_length())
            else:
                # legacy tuple cache: (layer, (K,V))
                past_len = int(past_key_values[0][0].shape[-2])

            max_cache_len = None
            if hasattr(past_key_values, "get_max_cache_shape"):
                m = past_key_values.get_max_cache_shape()
                max_cache_len = int(m) if m is not None and m > 0 else None
            elif hasattr(past_key_values, "get_max_length"):
                m = past_key_values.get_max_length()
                max_cache_len = int(m) if m is not None else None

            # ensure cache_position
            cache_position = kwargs.get("cache_position", None)
            if cache_position is None:
                kwargs["cache_position"] = torch.arange(
                    past_len,
                    past_len + seq_length,
                    device = device,
                    dtype = torch.long,
                )
            else:
                if hasattr(cache_position, "device") and cache_position.device != device:
                    kwargs["cache_position"] = cache_position.to(device)

            # Get to the base model
            base_model = self
            if hasattr(base_model, "base_model_prefix"):
                base_model = getattr(base_model, base_model.base_model_prefix)

            if hasattr(base_model, "_prepare_4d_causal_attention_mask_with_cache_position"):
                if not hasattr(base_model, "_unsloth_mask_needs_device"):

                    def _check_needs_device(fn) -> bool:
                        try:
                            sig = inspect.signature(inspect.unwrap(fn))
                            return "device" in sig.parameters
                        except:
                            # transformers <= 4.51.3 includes device arg but > 4.51.3 does not
                            return transformers_version < Version("4.52.0")

                    base_model._unsloth_mask_needs_device = _check_needs_device(
                        base_model._prepare_4d_causal_attention_mask_with_cache_position
                    )

                if max_cache_len is not None:
                    target_length = max_cache_len
                elif original_attention_mask is not None and original_attention_mask.dim() == 2:
                    target_length = original_attention_mask.shape[-1]
                else:
                    target_length = past_len + seq_length

                mask_kwargs = {
                    "sequence_length": seq_length,
                    "target_length": target_length,
                    "dtype": self.dtype,
                    "cache_position": kwargs["cache_position"],
                    "batch_size": bs,
                    "config": self.config,
                    "past_key_values": past_key_values,
                }
                if base_model._unsloth_mask_needs_device:
                    mask_kwargs["device"] = device

                attention_mask = base_model._prepare_4d_causal_attention_mask_with_cache_position(
                    attention_mask,
                    **mask_kwargs,
                )
            else:
                if transformers_version <= Version("4.52.4"):
                    logger.warning_once(
                        f"{self.__class__.__name__} has no `_prepare_4d_causal_attention_mask_with_cache_position` method "
                        "defined in its base modeling class. Compiled forward passes will be sub-optimal. If you're "
                        "writing code, see Llama for an example implementation. If you're a user, please report this "
                        "issue on GitHub."
                    )

    if kwargs.get("position_ids", None) is None:
        if original_attention_mask is not None and original_attention_mask.dim() == 2:
            position_ids = original_attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(original_attention_mask == 0, 1)
            position_ids = position_ids[:, -seq_length:]
            kwargs["position_ids"] = position_ids
        elif kwargs.get("cache_position", None) is not None:
            cp = kwargs["cache_position"]
            if cp.dim() == 1:
                cp = cp.unsqueeze(0).expand(bs, -1)
            kwargs["position_ids"] = cp

    result = {
        "attention_mask": attention_mask,
        **kwargs,
    }
    if use_inputs_embeds:
        result["inputs_embeds"] = inputs_embeds
        result["input_ids"] = None
    else:
        result["input_ids"] = input_ids
    return result


def fix_prepare_inputs_for_generation(module):
    # Fix prepare_inputs_for_generation
    if hasattr(module, "prepare_inputs_for_generation"):
        module.prepare_inputs_for_generation = _fast_prepare_inputs_for_generation


torch_matmul = torch.matmul


def LlamaAttention_fast_forward_inference(
    self,
    hidden_states: torch.Tensor,
    past_key_value: Optional[Tuple[torch.Tensor]],
    position_ids,
    do_prefill = False,
    attention_mask = None,
    rotary_seq_len = None,
):
    """
    https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L406
    Fast inference using KV cache.
    QK^T can be computed in 4 chunks

    [Q, q] @ [K, k].T where q, k are the new tokens.
    [QK^T, Qk^T]
    [qK^T, qk^T]

    Since the attention mask wipes Qk^T, we just get
    [QK^T,    0]
    [qK^T, qk^T]

    Since softmax is row-wise, we get
    softmax([QK^T,    0])
    softmax([qK^T, qk^T])

    We then multiply by   [V]
                          [v]
    softmax([QK^T,    0]) [softmax(QK^T)V] *
    softmax([qK^T, qk^T]) [softmax([qK^T, qk^T]) @ [V, v]]

    But notice * [softmax(QK^T)V] is just the last attention.
    We just need to compute the last final row.

    This means we can pass in a row of Q, but we need to
    remember K and V, which are called the KV cache.
    """
    Xn = hidden_states
    bsz, _, hd = hidden_states.size()
    K1, V1 = past_key_value
    dtype = Xn.dtype

    n_heads = self.config.num_attention_heads
    n_groups = self.num_key_value_groups
    n_kv_heads = self.config.num_key_value_heads
    head_dim = self.head_dim
    # assert(n_kv_heads * n_groups == n_heads)

    hidden_size = self.config.hidden_size
    attention_size = n_heads * head_dim
    seq_len = K1.shape[-2]
    kv_seq_len = seq_len + 1

    # Prefill phase
    # if not hasattr(self, "paged_attention"):
    device = hidden_states.device
    if do_prefill:
        self.paged_attention = torch.empty(
            (KV_CACHE_INCREMENT + seq_len + 1, 2, bsz, n_kv_heads, head_dim),
            dtype = dtype,
            device = device,
        )
        self.paged_attention_K = self.paged_attention[:, 0]
        self.paged_attention_V = self.paged_attention[:, 1]
        self.paged_attention_K[:seq_len] = K1.permute(2, 0, 1, 3)
        self.paged_attention_V[:seq_len] = V1.permute(2, 0, 1, 3)
        self.temp_QA = torch.empty((2, bsz, 1, attention_size), dtype = dtype, device = device)
        self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads * head_dim), dtype = dtype, device = device)
        self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = device)

        # Mistral Nemo 12b has weird dimensions
        if attention_size != hidden_size:
            self.temp_O = torch.empty((bsz, 1, hidden_size), dtype = dtype, device = device)
        else:
            self.temp_O = self.temp_QA[1][:, :, :hidden_size]

        self.attention = torch.empty(
            (bsz, n_heads, 1, KV_CACHE_INCREMENT + seq_len), dtype = dtype, device = device
        )
        self.scalar = 1.0 / math_sqrt(self.head_dim)
        self.half_head_dim = head_dim // 2
    elif kv_seq_len >= self.paged_attention.shape[0]:
        self.paged_attention.resize_(
            (
                self.paged_attention.shape[0] + KV_CACHE_INCREMENT,
                2,
                bsz,
                n_kv_heads,
                head_dim,
            )
        )
        self.paged_attention_K = self.paged_attention[:, 0]
        self.paged_attention_V = self.paged_attention[:, 1]
        self.attention.resize_((bsz, n_heads, 1, self.attention.shape[-1] + KV_CACHE_INCREMENT))

    Qn = fast_linear_forward(self.q_proj, Xn, out = self.temp_QA[0])
    Kn = fast_linear_forward(self.k_proj, Xn, out = self.temp_KV[0])
    Vn = fast_linear_forward(self.v_proj, Xn, out = self.temp_KV[1])
    Qn = Qn.view(bsz, 1, n_heads, head_dim).transpose(1, 2)
    Kn = Kn.view(bsz, 1, n_kv_heads, head_dim).transpose(1, 2)
    Vn = Vn.view(bsz, 1, n_kv_heads, head_dim).transpose(1, 2)

    # cos, sin = self.rotary_emb(Vn, seq_len = kv_seq_len)
    # Qn, Kn = inplace_rope_embedding(Qn, Kn, cos, sin, position_ids)

    # Need to do it prior 2 steps before hitting full on short KV cache
    # or else error
    if position_ids.dim() == 1:
        position_ids = position_ids[:, None]
    # Transformers 5.x accumulates position_ids as [batch, full_seq_len] across
    # decode steps; single-token inference only needs the last position.
    if position_ids.shape[-1] > 1:
        position_ids = position_ids[:, -1:]
    position_ids = position_ids.to(Qn.device)

    if rotary_seq_len is None:
        rotary_seq_len = max(kv_seq_len, int(position_ids.max().item()) + 1)
    self.rotary_emb.extend_rope_embedding(Vn, rotary_seq_len + 1)  # +1 slack
    cos, sin = self.rotary_emb.get_cached(rotary_seq_len, Qn.device.index or 0)

    cos = cos[position_ids].unsqueeze(1).to(device = Qn.device, dtype = Qn.dtype)
    sin = sin[position_ids].unsqueeze(1).to(device = Qn.device, dtype = Qn.dtype)

    h = self.half_head_dim

    RH_Q = self.RH_Q
    RH_Q[:, :, :, :h] = Qn[:, :, :, h:]
    RH_Q[:, :, :, h:] = Qn[:, :, :, :h]
    RH_Q[:, :, :, :h].neg_()  # torch.neg(RH_Q[:,:,:,:h], out = RH_Q[:,:,:,:h])
    Qn *= cos
    Qn.addcmul_(RH_Q, sin)

    RH_K = RH_Q[
        :, :n_kv_heads, :, :
    ]  # torch.empty((n_kv_heads, 1, head_dim), dtype = dtype, device = "cuda:0")
    RH_K[:, :, :, :h] = Kn[:, :, :, h:]
    RH_K[:, :, :, h:] = Kn[:, :, :, :h]
    RH_K[:, :, :, :h].neg_()  # torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h])
    Kn *= cos
    Kn.addcmul_(RH_K, sin)

    # New KV cache
    # Kn = torch.cat([K1, Kn], dim = 2)
    # Vn = torch.cat([V1, Vn], dim = 2)
    self.paged_attention_K[seq_len] = Kn.permute(2, 0, 1, 3)
    self.paged_attention_V[seq_len] = Vn.permute(2, 0, 1, 3)
    Kn = self.paged_attention_K[:kv_seq_len].permute(1, 2, 0, 3)
    Vn = self.paged_attention_V[:kv_seq_len].permute(1, 2, 0, 3)

    # Handle sliding windows
    sliding_window = getattr(self.config, "sliding_window", None)
    if sliding_window is not None and kv_seq_len > sliding_window:
        # From https://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/modeling_mistral.py#L193
        start = kv_seq_len - sliding_window
        Knn = Kn[:, :, start:, :]  # .contiguous()
        Vnn = Vn[:, :, start:, :]  # .contiguous()
        if attention_mask is not None:
            attention_mask = attention_mask[..., start:]
    else:
        Knn, Vnn = Kn, Vn

    # Grouped query attention
    _, _, cached_len, _ = Knn.shape
    if bsz == 1 or ((not SDPA_HAS_GQA) and n_groups != 1):
        Knn = Knn[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, cached_len, head_dim)
        Vnn = Vnn[:, :, None, :, :].expand(bsz, n_kv_heads, n_groups, cached_len, head_dim)
        Knn = Knn.reshape(bsz, n_heads, cached_len, head_dim)
        Vnn = Vnn.reshape(bsz, n_heads, cached_len, head_dim)

    # when qlen==vlen and attn_mask is None, we should use causal attention
    Q_len = Qn.shape[-2]
    K_len = Knn.shape[-2]
    if attention_mask is None and Q_len == K_len:
        is_causal = True
    else:
        is_causal = False
    # Attention
    if bsz == 1:
        Qn *= (
            self.scalar
        )  # See https://github.com/ggerganov/llama.cpp/issues/7805#issuecomment-2153349963
        # It seems like doing (Q * scalar) @ K is better than (Q @ K) * scalar to stop overflows
        A = torch_matmul(Qn, Knn.transpose(2, 3), out = self.attention[:, :, :, :cached_len])
        A[:] = torch_nn_functional_softmax(A, dim = -1, dtype = torch.float32)  # .to(A.dtype)
        A = torch_matmul(A, Vnn, out = Qn)
    # --- attention_mask fixup for SDPA if user passes 2D padding mask
    else:
        if attention_mask is not None and attention_mask.dim() == 2:
            attention_mask = attention_mask[:, None, None, :].to(torch.bool)
            # is it more appropriate to use _prepare_4d_causal_attention_mask_for_sdpa?
        elif (
            attention_mask is not None
            and attention_mask.dim() == 4
            and attention_mask.dtype != torch.bool
        ):
            # Decode is more stable with boolean keep masks than additive bf16 masks.
            attention_mask = attention_mask.eq(0)

        if SDPA_HAS_GQA:
            A = scaled_dot_product_attention(
                Qn,
                Knn,
                Vnn,
                attn_mask = attention_mask,
                is_causal = is_causal,
                enable_gqa = True,
            )
        else:
            A = scaled_dot_product_attention(
                Qn, Knn, Vnn, attn_mask = attention_mask, is_causal = is_causal
            )
    A = A.transpose(1, 2)
    A = A.reshape(bsz, 1, attention_size)
    A = fast_linear_forward(self.o_proj, A, out = self.temp_O)
    return A, (Kn, Vn)


torch_nn_functional_silu = torch.nn.functional.silu


def fast_swiglu_inference(
    self,
    X,
    temp_gate = None,
    temp_up = None,
    gate_multiplier = None,
    down_multiplier = None,
):
    # gate = self.gate_proj(X)
    # up   = self.up_proj(X)
    bsz, _, hd = X.shape
    # mlp_size = self.config.intermediate_size
    # temp = torch.empty((2, bsz, 1, mlp_size), dtype = X.dtype, device = "cuda:0")

    gate = fast_linear_forward(self.gate_proj, X, out = temp_gate)

    if gate_multiplier is not None:
        gate *= gate_multiplier

    up = fast_linear_forward(self.up_proj, X, out = temp_up)

    gate = torch_nn_functional_silu(gate, inplace = True)
    gate *= up

    # X = self.down_proj(gate)
    down = fast_linear_forward(self.down_proj, gate, out = up[:, :, :hd])

    if down_multiplier is not None:
        down *= down_multiplier

    return down


torch_square = torch.square
torch_mean = torch.mean


def fast_rms_layernorm_inference(
    self,
    X,
    XX = None,
    XX2 = None,
    variance = None,
):
    old_dtype = X.dtype
    if XX is None:
        XX = X.to(torch.float32)
        variance = XX.square().mean(-1, keepdim = True)
    else:
        XX.copy_(X)
        torch_mean(torch_square(XX, out = XX2), -1, keepdim = True, out = variance)
    variance += self.variance_epsilon
    XX *= variance.rsqrt_()

    if XX is None:
        X = XX.to(old_dtype)
    else:
        X.copy_(XX)

    X *= self.weight
    return X


def fast_rms_layernorm_inference_gemma(
    self,
    X,
    out_weight = None,
):
    XX = X.to(torch.float32)
    variance = XX.square().mean(-1, keepdim = True)
    variance += self.variance_epsilon
    XX *= variance.rsqrt_()

    if out_weight is None:
        out_weight = self.weight + 1.0
    else:
        out_weight[:] = self.weight
        out_weight += 1.0

    XX *= out_weight
    return XX.to(X.dtype)


# Normal layernorm with mean removal
@torch.compile(fullgraph = False, dynamic = True, options = torch_compile_options)
def fast_layernorm_compiled(layernorm, X):
    old_dtype = X.dtype
    X = X.float()
    mean = X.mean(-1, keepdim = True)
    Xbar = X - mean
    X = (
        Xbar
        * torch.rsqrt(Xbar.square().mean(-1, keepdim = True) + layernorm.variance_epsilon)
        * layernorm.weight.float()
    )
    return X.to(old_dtype)


# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L320
def LlamaAttention_fast_forward(
    self,
    hidden_states: torch.Tensor,
    causal_mask: Optional[BlockDiagonalCausalMask] = None,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: bool = False,
    use_cache: bool = False,
    padding_mask: Optional[torch.LongTensor] = None,
    position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
    *args,
    **kwargs,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
    # Clear inference
    if hasattr(self, "paged_attention"):
        del self.paged_attention_K
        del self.paged_attention_V
        del self.paged_attention
        del self.temp_QA
        del self.temp_KV
        del self.RH_Q
        del self.attention
    bsz, q_len, _ = hidden_states.size()

    n_heads = self.config.num_attention_heads
    n_groups = self.num_key_value_groups
    n_kv_heads = self.config.num_key_value_heads
    head_dim = self.head_dim
    assert n_kv_heads * n_groups == n_heads

    Q, K, V = self.apply_qkv(self, hidden_states)
    Q = Q.view(bsz, q_len, n_heads, head_dim).transpose(1, 2)
    K = K.view(bsz, q_len, n_kv_heads, head_dim).transpose(1, 2)
    V = V.view(bsz, q_len, n_kv_heads, head_dim).transpose(1, 2)
    seq_info = get_packed_info_from_kwargs(kwargs, Q.device)

    kv_seq_len = K.shape[-2]
    if past_key_value is not None:
        kv_seq_len += past_key_value[0].shape[-2]

    if position_embeddings and kv_seq_len <= position_embeddings[0].shape[0]:
        cos, sin = position_embeddings
    else:
        rotary_emb = self.rotary_emb
        rotary_emb.extend_rope_embedding(V, seq_len = kv_seq_len)
        cos, sin = rotary_emb.get_cached(kv_seq_len, Q.device.index)
        cos = cos.to(device = Q.device, dtype = Q.dtype)
        sin = sin.to(device = Q.device, dtype = Q.dtype)

    rope_position_ids = position_ids
    if rope_position_ids is None and seq_info is not None:
        rope_position_ids = kwargs.get("position_ids")

    # Q, K = (
    #     fast_rope_embedding(Q, K, cos, sin)
    #     if rope_position_ids is None
    #     else inplace_rope_embedding(Q, K, cos, sin, rope_position_ids)
    # )
    Q, K = fast_rope_embedding(Q, K, cos, sin, rope_position_ids)

    if past_key_value is not None:
        K = torch.cat([past_key_value[0], K], dim = 2)
        V = torch.cat([past_key_value[1], V], dim = 2)
    past_key_value = (K, V) if use_cache else None

    # Attention module
    use_varlen = seq_info is not None and past_key_value is None
    backend = SDPA if attention_mask is not None else select_attention_backend(use_varlen)

    # should dropout be hardcoded to 0.0?
    config = AttentionConfig(
        backend = backend,
        n_kv_heads = n_kv_heads,
        n_groups = n_groups,
        flash_dense_kwargs = {"causal": True},
        flash_varlen_kwargs = {"dropout_p": 0.0, "causal": True},
    )
    # PrefixGrouper seg table rides in **kwargs from the GRPO logprob forward (same route
    # as packed_seq_lengths); misuse (KV cache / padding mask) raises. None => byte-identical
    # default. Reuse of this forward also carries the branch to qwen2 & gemma.
    _pg_seg = resolve_prefix_seg_info(kwargs, past_key_value, attention_mask)
    context = AttentionContext(
        bsz = bsz,
        q_len = q_len,
        kv_seq_len = kv_seq_len,
        n_heads = n_heads,
        head_dim = head_dim,
        requires_grad = hidden_states.requires_grad,
        seq_info = seq_info,
        attention_mask = attention_mask,
        causal_mask = causal_mask,
        prefix_seg_info = _pg_seg,
    )

    A = run_attention(config = config, context = context, Q = Q, K = K, V = V)
    attn_output = A.reshape(bsz, q_len, n_heads * head_dim)
    attn_output = self.apply_o(self, attn_output)
    attn_weights = None
    return attn_output, attn_weights, past_key_value


# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L590
def LlamaDecoderLayer_fast_forward(
    self,
    hidden_states: torch.Tensor,
    causal_mask = None,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_value: Optional[Tuple[torch.Tensor]] = None,
    output_attentions: Optional[bool] = False,
    use_cache: Optional[bool] = False,
    padding_mask: Optional[torch.LongTensor] = None,
    position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
    *args,
    **kwargs,
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
    """
    Args:
        hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
        attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
            `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under
            returned tensors for more detail.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
            (see `past_key_values`).
        past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
    """
    if use_cache and hasattr(self, "_flag_for_generation"):
        residual = hidden_states
        hidden_states = fast_rms_layernorm_inference(self.input_layernorm, hidden_states)
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states = hidden_states,
            causal_mask = causal_mask,
            attention_mask = attention_mask,
            position_ids = position_ids,
            past_key_value = past_key_value,
            output_attentions = output_attentions,
            use_cache = use_cache,
            padding_mask = padding_mask,
            position_embeddings = position_embeddings,
            **kwargs,
        )
        hidden_states += residual

        # Fully Connected
        residual = hidden_states
        hidden_states = fast_rms_layernorm_inference(self.post_attention_layernorm, hidden_states)
        hidden_states = fast_swiglu_inference(self.mlp, hidden_states)
        hidden_states += residual
    else:
        residual = hidden_states
        hidden_states = fast_rms_layernorm(self.input_layernorm, hidden_states)
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states = hidden_states,
            causal_mask = causal_mask,
            attention_mask = attention_mask,
            position_ids = position_ids,
            past_key_value = past_key_value,
            output_attentions = output_attentions,
            use_cache = use_cache,
            padding_mask = padding_mask,
            position_embeddings = position_embeddings,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = fast_rms_layernorm(self.post_attention_layernorm, hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

    outputs = (hidden_states,)
    if output_attentions:
        outputs += (self_attn_weights,)
    if use_cache:
        outputs += (present_key_value,)
    return outputs


# https://github.com/unslothai/unsloth/issues/404#issuecomment-2323473452
__DTYPE_MAP = {
    "float32": torch.float32,
    torch.float32: torch.float32,
    "float16": torch.float16,
    torch.float16: torch.float16,
    "bfloat16": torch.bfloat16,
    torch.bfloat16: torch.bfloat16,
}


# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L825
def LlamaModel_fast_forward(
    self,
    input_ids: Optional[torch.LongTensor] = None,
    causal_mask: Optional[BlockDiagonalCausalMask] = None,
    attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None,
    past_key_values: Optional[List[torch.FloatTensor]] = None,
    inputs_embeds: Optional[torch.FloatTensor] = None,
    use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None,
    *args,
    **kwargs,
) -> Union[Tuple, BaseModelOutputWithPast]:
    output_attentions = (
        output_attentions if output_attentions is not None else self.config.output_attentions
    )
    assert output_attentions is False
    output_hidden_states = (
        output_hidden_states
        if output_hidden_states is not None
        else self.config.output_hidden_states
    )
    use_cache = use_cache if use_cache is not None else self.config.use_cache

    return_dict = return_dict if return_dict is not None else self.config.use_return_dict

    # retrieve input_ids and inputs_embeds
    if input_ids is not None and inputs_embeds is not None:
        raise ValueError(
            "Unsloth: You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
        )
    elif input_ids is not None:
        batch_size, seq_length = input_ids.shape
    elif inputs_embeds is not None:
        batch_size, seq_length, _ = inputs_embeds.shape
    else:
        raise ValueError(
            "Unsloth: You have to specify either decoder_input_ids or decoder_inputs_embeds"
        )

    seq_length_with_past = seq_length

    # Fix out of bounds tokenization unless we were given packed metadata
    allow_overlength = (
        getattr(self, "_unsloth_allow_packed_overlength", False)
        or ("packed_seq_lengths" in kwargs)
        or ("prefix_seg_info" in kwargs and kwargs["prefix_seg_info"] is not None)
    )
    if hasattr(self, "max_seq_length") and not allow_overlength:
        if seq_length > self.max_seq_length:
            shape = input_ids.shape if input_ids is not None else inputs_embeds.shape
            logger.warning_once(
                f"Unsloth: Input IDs of shape {shape} with length {seq_length} > the model's max sequence length of {self.max_seq_length}.\n"
                "We shall truncate it ourselves. It's imperative if you correct this issue first."
            )
        if input_ids is not None:
            input_ids = input_ids[:, : self.max_seq_length]
        elif inputs_embeds is not None:
            inputs_embeds = inputs_embeds[:, : self.max_seq_length, :]
        if attention_mask is not None and attention_mask.shape[-1] > self.max_seq_length:
            attention_mask = attention_mask[:, : self.max_seq_length]

    past_key_values_length = 0

    if past_key_values is not None:
        past_key_values_length = past_key_values[0][0].shape[2]
        seq_length_with_past = seq_length_with_past + past_key_values_length

    # We already handle KV cache position_ids ourselves.
    if False:  # (past_key_values_length != 0):
        position_ids = torch.arange(
            past_key_values_length,
            seq_length + past_key_values_length,
            dtype = torch.int32,
            device = f"{DEVICE_TYPE_TORCH}:0",
        )
        position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
    elif position_ids is not None:
        position_ids = position_ids.view(-1, seq_length).to(torch.int32)  # .long()
    else:
        position_ids = None

    if position_ids is not None:
        if position_ids.shape[0] != batch_size:
            position_ids = position_ids.repeat((batch_size, 1))

    # Embed positions
    if inputs_embeds is None:
        inputs_embeds = self.embed_tokens(input_ids)

    inputs_embeds = inputs_embeds.to(_get_dtype(dtype_from_config(self.config)))

    # Normalized from Gemma
    IS_GEMMA = self.config.model_type.startswith("gemma")
    IS_GEMMA2 = self.config.model_type.startswith("gemma2")
    IS_COHERE = self.config.model_type.startswith("cohere")
    IS_GRANITE = self.config.model_type.startswith("granite")
    IS_FALCON_H1 = self.config.model_type.startswith("falcon_h1")

    train_embed_tokens = self.embed_tokens.weight.requires_grad

    if IS_GEMMA:
        # Match Gemma exactly by casting to bfloat16 / float16
        # inputs_embeds *= math_sqrt(self.config.hidden_size)
        # Ie 3072**0.5 = 55.5000 in bfloat16, whilst 55.4256 in float32
        # &  2048**0.5 = 45.2500 in bfloat16, whilst 45.2548 in float32
        normalizer = torch.tensor(math_sqrt(self.config.hidden_size), dtype = inputs_embeds.dtype)

        if train_embed_tokens:
            # Careful we must not do an inplace op!
            inputs_embeds = inputs_embeds * normalizer
        else:
            inputs_requires_grad = inputs_embeds.requires_grad
            if not inputs_embeds.is_leaf:
                inputs_embeds = inputs_embeds.detach()
                inputs_requires_grad = True
            elif inputs_requires_grad:
                inputs_embeds.requires_grad_(False)
            inputs_embeds *= normalizer
            # inputs_embeds *= math_sqrt(self.config.hidden_size)
            if inputs_requires_grad:
                inputs_embeds.requires_grad_(True)

    # Fix up attention mask by setting elements to 0
    # Specifically for DPO
    if (
        getattr(self, "_has_no_labels", False) is True
        and (attention_mask is not None)
        and attention_mask.ndim == 2
        and (past_key_values is None)
        and (not train_embed_tokens)
        and self.training
    ):
        # Careful for inference the attention_mask is size (1, kv_seq_len)
        # Whilst the input_embeds is size (1, 1, 4096)
        inputs_requires_grad = inputs_embeds.requires_grad
        if not inputs_embeds.is_leaf:
            inputs_embeds = inputs_embeds.detach()
            inputs_requires_grad = True
        elif inputs_requires_grad:
            inputs_embeds.requires_grad_(False)
        attention_mask = attention_mask[:, : self.max_seq_length]  # Must resize!
        inputs_embeds *= attention_mask.unsqueeze(0).transpose(0, 1).transpose(1, 2)
        if inputs_requires_grad:
            inputs_embeds.requires_grad_(True)

    # Ignore attention_mask
    if attention_mask is None:
        padding_mask = None
    elif self.training:
        attention_mask = None
        padding_mask = None
    else:
        # if 0 in attention_mask:
        #     padding_mask = attention_mask
        # else:
        padding_mask = None

        attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
            attention_mask,
            (batch_size, seq_length),
            inputs_embeds,
            past_key_values_length,
            sliding_window = getattr(self.config, "sliding_window", None),
        )
        # Must NOT convert to bool - weirdly this causes stuff to error out!
        # if attention_mask is not None:
        #     attention_mask = attention_mask.to(torch.bool)

    hidden_states = inputs_embeds
    if IS_GRANITE or IS_FALCON_H1:  # granite has embedding multiplier
        hidden_states = self.config.embedding_multiplier * hidden_states

    if past_key_values is None and self.training:
        use_cache = False
        # if use_cache:
        #     logger.warning_once(
        #         "Unsloth: `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`"
        #     )
        #     use_cache = False

    # decoder layers
    all_hidden_states = () if output_hidden_states else None
    all_self_attns = () if output_attentions else None
    next_decoder_cache = () if use_cache else None

    # Gradient checkpointing methods (ie sqrt)
    if hasattr(self, "_gradient_checkpointing_boundaries"):
        boundaries = self._gradient_checkpointing_boundaries
    else:
        boundaries = None

    # Check checkpointing method
    gradient_checkpointing = False

    if self.gradient_checkpointing and self.training and not use_cache:
        gradient_checkpointing = True

    # Gemma2 has alternating SWA and global attn
    use_static_mask = True
    dynamic_SWA_mask = None
    dynamic_GA_mask = None
    if IS_GEMMA2:
        if HAS_FLASH_ATTENTION_SOFTCAPPING and attention_mask is None:
            self.SWA_mask = True
            self.GA_mask = False
        elif attention_mask is not None:
            # Fixes https://github.com/unslothai/unsloth/issues/853
            # Unsloth needs a 2D mask, not a [2, 1, n, n] mask!

            # https://github.com/pytorch/pytorch/issues/103749
            # Need to convert to float and not using bool
            # attention_mask = (1.0 - attention_mask.float()) * torch.finfo(inputs_embeds.dtype).min
            dynamic_SWA_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
                sliding_window = self.config.sliding_window,
            )
            dynamic_GA_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
                sliding_window = None,
            )
            use_static_mask = False

        elif not hasattr(self, "SWA_mask"):
            if HAS_FLEX_ATTENTION:
                # Use Flex Attention instead!
                self.SWA_mask = create_flex_attention_sliding_window_mask(
                    self.max_seq_length, self.config.sliding_window
                )
                self.GA_mask = create_flex_attention_causal_mask(self.max_seq_length)
            else:
                n = self.max_seq_length  # self.config.max_position_embeddings
                # masked_fill is making stuff slower!
                # self. GA_mask = create_boolean_mask(n = n, sliding_window = 0)
                # self.SWA_mask = create_boolean_mask(n = n, sliding_window = self.config.sliding_window)
                from transformers.modeling_attn_mask_utils import AttentionMaskConverter

                self.SWA_mask = (
                    AttentionMaskConverter(
                        is_causal = True,
                        sliding_window = self.config.sliding_window,
                    )
                    .to_causal_4d(
                        1,
                        n,
                        n,
                        dtype = inputs_embeds.dtype,
                        device = DEVICE_TYPE_TORCH,
                    )
                    .squeeze(0)
                    .squeeze(0)
                )

                self.GA_mask = (
                    AttentionMaskConverter(
                        is_causal = True,
                    )
                    .to_causal_4d(
                        1,
                        n,
                        n,
                        dtype = inputs_embeds.dtype,
                        device = DEVICE_TYPE_TORCH,
                    )
                    .squeeze(0)
                    .squeeze(0)
                )
            pass

    if (
        IS_ATTENTION_REFACTOR
        and (hasattr(self, "rotary_emb") or not hasattr(self.layers[0].self_attn, "rotary_emb"))
    ) or IS_GRANITE:
        # position_embeddings is mandatory on main: https://github.com/huggingface/transformers/pull/34858
        # granite always had the attention refactor, so let it always use this path.
        self.rotary_emb.extend_rope_embedding(hidden_states, self.config.max_position_embeddings)
        position_embeddings = self.rotary_emb.get_cached(
            self.config.max_position_embeddings, hidden_states.device.index
        )
    else:
        position_embeddings = None

    # Go through every layer!
    for idx, decoder_layer in enumerate(self.layers):
        if output_hidden_states:
            all_hidden_states += (hidden_states,)
        past_key_value = past_key_values[idx] if past_key_values is not None else None

        mask = causal_mask
        if IS_GEMMA2:
            use_sliding_window = idx % 2 == 0
            if use_sliding_window:
                mask = self.SWA_mask if use_static_mask else dynamic_SWA_mask
            else:
                mask = self.GA_mask if use_static_mask else dynamic_GA_mask
            kwargs["use_sliding_window"] = use_sliding_window

        if gradient_checkpointing and not isinstance(decoder_layer, GradientCheckpointingLayer):

            def create_custom_forward(module):
                def custom_forward(*inputs):
                    return module(
                        *inputs,
                        past_key_value,
                        output_attentions,
                        padding_mask = padding_mask,
                        position_embeddings = position_embeddings,
                        **kwargs,
                    )

                return custom_forward

            layer_outputs = torch.utils.checkpoint.checkpoint(
                create_custom_forward(decoder_layer),
                hidden_states,
                mask,
                attention_mask,
                position_ids,
                use_reentrant = True,
                preserve_rng_state = False,
            )
            hidden_states = layer_outputs[0]

        else:
            layer_outputs = decoder_layer(
                hidden_states,
                causal_mask = mask,
                attention_mask = attention_mask,
                position_ids = position_ids,
                past_key_value = past_key_value,
                output_attentions = output_attentions,
                use_cache = use_cache,
                padding_mask = padding_mask,
                position_embeddings = position_embeddings,
                **kwargs,
            )
            hidden_states = layer_outputs[0]

        if use_cache:
            next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
        if output_attentions:
            all_self_attns += (layer_outputs[1],)

    # Final layernorm
    if use_cache:
        if IS_FALCON_H1:
            hidden_states = fast_rms_layernorm_inference(self.final_layernorm, hidden_states)
        else:
            hidden_states = (
                fast_rms_layernorm_inference_gemma if IS_GEMMA else fast_rms_layernorm_inference
            )(self.norm, hidden_states)
    elif IS_COHERE:
        hidden_states = self.norm(hidden_states)
    elif IS_FALCON_H1:
        hidden_states = fast_rms_layernorm(self.final_layernorm, hidden_states, gemma = IS_GEMMA)
    else:
        hidden_states = fast_rms_layernorm(self.norm, hidden_states, gemma = IS_GEMMA)

    if output_hidden_states:
        all_hidden_states += (hidden_states,)
    next_cache = next_decoder_cache if use_cache else None

    if not return_dict:
        return tuple(
            v
            for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
            if v is not None
        )
    return BaseModelOutputWithPast(
        last_hidden_state = hidden_states,
        past_key_values = next_cache,
        hidden_states = all_hidden_states,
        attentions = all_self_attns,
    )


# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py#L825
def _LlamaModel_fast_forward_inference(
    attention_fast_forward_inference = LlamaAttention_fast_forward_inference,
    mlp_fast_forward_inference = fast_swiglu_inference,
):
    # Makes attention and MLP customisable for models like qwen3/cohere.
    def LlamaModel_fast_forward_inference_custom(
        self,
        input_ids,
        past_key_values,
        position_ids,
        attention_mask = None,
        **kwargs,
    ):
        input_ids = input_ids[:, : self.max_seq_length]
        bsz, q_len = input_ids.shape
        hd = self.config.hidden_size
        mlp_size = self.config.intermediate_size

        X = self.model.embed_tokens(input_ids)
        X = X.to(_get_dtype(dtype_from_config(self.config)))
        bsz, q_len, hd = X.shape
        assert q_len == 1
        # Get saved buffers to reduce memory movement
        residual = torch.empty(
            (bsz, q_len, hd), dtype = torch.float32, device = f"{DEVICE_TYPE_TORCH}:0"
        )
        _XX = torch.empty((2, bsz, q_len, hd), dtype = torch.float32, device = f"{DEVICE_TYPE_TORCH}:0")
        XX, XX2 = _XX[0], _XX[1]
        variance = torch.empty(
            (bsz, q_len, 1), dtype = torch.float32, device = f"{DEVICE_TYPE_TORCH}:0"
        )
        temp_mlp = torch.empty(
            (2, bsz, 1, mlp_size), dtype = X.dtype, device = f"{DEVICE_TYPE_TORCH}:0"
        )
        temp_gates, temp_ups = (
            tuple(temp_mlp[0].to(torch.device(x)) for x in range(DEVICE_COUNT)),
            tuple(temp_mlp[1].to(torch.device(x)) for x in range(DEVICE_COUNT)),
        )

        seq_len = past_key_values[0][0].shape[-2]
        kv_seq_len = seq_len + 1
        if attention_mask is not None:
            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (bsz, q_len),
                X,
                seq_len,
                sliding_window = getattr(self.config, "sliding_window", None),
            )
            # Pre-convert to bool once for all layers (avoids per-layer .eq(0))
            if attention_mask is not None and attention_mask.dtype != torch.bool:
                attention_mask = attention_mask.eq(0)
        else:
            attention_mask = None

        # Compute rotary_seq_len once to avoid per-layer GPU-CPU sync from .item()
        rotary_seq_len = max(kv_seq_len, int(position_ids.max().item()) + 1)

        next_decoder_cache = []

        for idx, decoder_layer in enumerate(self.model.layers):
            device_index = getattr(decoder_layer, "_per_layer_device_index", 0)
            X, residual, position_ids = move_to_device(device_index, X, residual, position_ids)
            residual.copy_(X)  # residual = X
            X = fast_rms_layernorm_inference(
                decoder_layer.input_layernorm,
                X,
                XX = XX,
                XX2 = XX2,
                variance = variance,
            )
            X, present_key_value = attention_fast_forward_inference(
                decoder_layer.self_attn,
                hidden_states = X,
                past_key_value = past_key_values[idx],
                position_ids = position_ids,
                attention_mask = attention_mask,
                do_prefill = not hasattr(decoder_layer.self_attn, "paged_attention"),
                rotary_seq_len = rotary_seq_len,
            )
            X += residual

            residual.copy_(X)  # residual = X
            X = fast_rms_layernorm_inference(
                decoder_layer.post_attention_layernorm,
                X,
                XX = XX,
                XX2 = XX2,
                variance = variance,
            )
            X = mlp_fast_forward_inference(
                decoder_layer.mlp,
                X,
                temp_gate = temp_gates[device_index],
                temp_up = temp_ups[device_index],
            )
            X += residual

            next_decoder_cache.append(present_key_value)
        X = fast_rms_layernorm_inference(
            self.model.norm,
            X,
            XX = XX,
            XX2 = XX2,
            variance = variance,
        )

        return BaseModelOutputWithPast(
            last_hidden_state = X,
            past_key_values = next_decoder_cache,
            hidden_states = [],
            attentions = [],
        )

    return LlamaModel_fast_forward_inference_custom


# For ensuring backwards compatibility, we create LlamaModel_fast_forward_inference that is consumed by other models
LlamaModel_fast_forward_inference = _LlamaModel_fast_forward_inference()


def CausalLM_fast_forward(fast_forward_inference):
    def _CausalLM_fast_forward(
        self,
        input_ids: torch.LongTensor = None,
        causal_mask: Optional[BlockDiagonalCausalMask] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        num_logits_to_keep: Optional[int] = 0,
        logits_to_keep: Optional[int] = 0,
        *args,
        **kwargs,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        if past_key_values is not None:
            outputs = fast_forward_inference(
                self,
                input_ids,
                past_key_values,
                position_ids = position_ids,
                attention_mask = attention_mask,
                **kwargs,
            )
        else:
            causal_mask = xformers.attn_bias.LowerTriangularMask() if HAS_XFORMERS else None

            output_attentions = (
                output_attentions
                if output_attentions is not None
                else self.config.output_attentions
            )
            output_hidden_states = (
                output_hidden_states
                if output_hidden_states is not None
                else self.config.output_hidden_states
            )
            return_dict = return_dict if return_dict is not None else self.config.use_return_dict
            # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
            self.model._has_no_labels = labels is None
            outputs = self.model(
                input_ids = input_ids,
                causal_mask = causal_mask,
                attention_mask = attention_mask,
                position_ids = position_ids,
                past_key_values = past_key_values,
                inputs_embeds = inputs_embeds,
                use_cache = use_cache,
                output_attentions = output_attentions,
                output_hidden_states = output_hidden_states,
                return_dict = return_dict,
                **kwargs,
            )
        hidden_states = outputs[0]

        bsz, q_len, hd = hidden_states.shape
        lm_head = self.lm_head.weight
        lm_head_device = lm_head.device

        logit_softcapping = getattr(self.config, "final_logit_softcapping", 0)
        logit_scaling = getattr(self.config, "logit_scale", 0)
        dtype = lm_head.dtype
        # Skip int max() if either is a tensor (HF selective-decode form).
        if isinstance(num_logits_to_keep, torch.Tensor) or isinstance(logits_to_keep, torch.Tensor):
            num_logits_to_keep = 0
        else:
            num_logits_to_keep = max(num_logits_to_keep, logits_to_keep)

        # Move items to same device as lm_head
        hidden_states = hidden_states.to(lm_head_device)
        if labels is not None:
            labels = labels.to(lm_head_device)

        # Output last hidden states without logits if asked
        if os.environ.get("UNSLOTH_RETURN_HIDDEN_STATES", "0") == "1":
            if num_logits_to_keep != 0:
                hidden_states = hidden_states[:, -num_logits_to_keep:, :]
            return CausalLMOutputWithPast(
                loss = None,
                logits = hidden_states,
                past_key_values = outputs.past_key_values,
                hidden_states = outputs.hidden_states,
                attentions = outputs.attentions,
            )

        if bsz == 1 and q_len == 1:
            logits = torch.mv(lm_head, hidden_states.ravel().to(dtype))
            logits = logits.unsqueeze(0).unsqueeze(0)
        elif num_logits_to_keep != 0:
            logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :].to(dtype))
        else:
            RETURN_LOGITS = os.environ.get("UNSLOTH_RETURN_LOGITS", "0") == "1"
            # < 1024 Normal Unsloth uses less VRAM!
            if bsz * q_len <= 1024 and not RETURN_LOGITS:
                # Use unsloth_fused_ce_loss which actually calculates the best chunk size to reduce VRAM usage
                RETURN_LOGITS = False

            if not RETURN_LOGITS and labels is not None:
                n_items = kwargs.get("num_items_in_batch", None)
                if n_items is None:
                    n_items = kwargs.get("n_items", None)

                if self.config.model_type == "falcon_h1":
                    hidden_states = hidden_states * self.config.lm_head_multiplier

                ### DISABLED since T4 breaks
                # OutOfResources: out of resource: shared memory, Required: 98304, Hardware limit: 65536. Reducing block sizes or `num_stages` may help.
                # loss = fused_linear_cross_entropy(
                #     hidden_states      = hidden_states,
                #     lm_weight          = lm_head,
                #     labels             = labels,
                #     num_items_in_batch = n_items,
                #     logit_softcapping  = logit_softcapping,
                # )
                loss = unsloth_fused_ce_loss(
                    trainer = None,
                    hidden_states = hidden_states,
                    lm_head_weight = lm_head,
                    lm_head_bias = None,
                    labels = labels,
                    mask = None,
                    n_items = n_items,
                    scaling = getattr(self, "accelerator_scaler", None),
                    target_gb = None,
                    torch_compile = True,
                    logit_softcapping = logit_softcapping,
                )
                if not return_dict:
                    # Fused CE never materializes `logits`; use EMPTY_LOGITS
                    # like the return_dict branch below (fixes #2068).
                    output = (EMPTY_LOGITS,) + outputs[1:]
                    return (loss,) + output if loss is not None else output

                output = CausalLMOutputWithPast(
                    loss = loss,
                    logits = EMPTY_LOGITS,
                    past_key_values = outputs.past_key_values,
                    hidden_states = outputs.hidden_states,
                    attentions = outputs.attentions,
                )
                return output
            pass
            logits = self.lm_head(hidden_states.to(dtype))

        logits = logits.to(_get_dtype(dtype_from_config(self.config)))
        loss = None
        logit_softcapping = getattr(self.config, "final_logit_softcapping", 0)
        logit_scaling = getattr(self.config, "logit_scale", 0)
        if self.config.model_type == "granite":
            # granite divides by logits_scaling (16) unlike cohere which multiplies by 0.125.
            # granite: https://github.com/huggingface/transformers/blob/4d1d0f29a493098e6bc6b904b82e29cb331827f5/src/transformers/models/granite/modeling_granite.py#L1103
            # cohere: https://github.com/huggingface/transformers/blob/4d1d0f29a493098e6bc6b904b82e29cb331827f5/src/transformers/models/cohere/modeling_cohere.py#L1176
            logit_scaling = 1 / getattr(self.config, "logits_scaling", 1)
        elif self.config.model_type == "falcon_h1":
            logit_scaling = self.config.lm_head_multiplier

        if labels is not None:
            shift_logits = logits
            # if not hasattr(self, "extra_ignored_labels"):
            #     # Fixes https://github.com/unslothai/unsloth/issues/10
            #     self.extra_ignored_labels = torch.full((self.max_seq_length, 1), -100, device = "cuda:0")
            # pass
            shift_labels = torch.empty_like(labels)
            shift_labels[..., :-1] = labels[..., 1:]
            shift_labels[..., -1] = -100
            mask_packed_sequence_boundaries(
                shift_labels,
                kwargs.get("packed_seq_lengths"),
            )
            # shift_labels = torch.hstack((labels[..., 1:], self.extra_ignored_labels[:labels.shape[0]]))
            n_items = kwargs.get("num_items_in_batch", None)
            if n_items is None:
                n_items = kwargs.get("n_items", None)
            loss = fast_cross_entropy_loss(
                logits = shift_logits,
                labels = shift_labels,
                logit_softcapping = logit_softcapping,
                logit_scaling = logit_scaling,
                n_items = n_items,
            )
        else:
            if logit_scaling != 0:
                if logits.requires_grad:
                    logits = logit_scaling * logits
                else:
                    logits *= logit_scaling
            if logit_softcapping != 0:
                if logits.requires_grad:
                    logits = (1.0 / logit_softcapping) * logits
                    logits = torch.tanh(logits)
                    logits = logit_softcapping * logits
                else:
                    logits *= 1.0 / logit_softcapping
                    logits.tanh_()
                    logits *= logit_softcapping

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output
        return CausalLMOutputWithPast(
            loss = loss,
            logits = logits,
            past_key_values = outputs.past_key_values,
            hidden_states = outputs.hidden_states,
            attentions = outputs.attentions,
        )

    return _CausalLM_fast_forward


@torch._disable_dynamo
def PeftModel_fast_forward(
    self,
    input_ids = None,
    causal_mask = None,
    attention_mask = None,
    inputs_embeds = None,
    labels = None,
    output_attentions = None,
    output_hidden_states = None,
    return_dict = None,
    task_ids = None,
    num_logits_to_keep = 0,
    logits_to_keep = 0,
    **kwargs,
):
    is_classification = "Classification" in str(type(self.base_model.model))
    if is_classification:
        return self.base_model(
            input_ids = input_ids,
            attention_mask = attention_mask,
            inputs_embeds = inputs_embeds,
            labels = labels,
            output_attentions = output_attentions,
            output_hidden_states = output_hidden_states,
            return_dict = return_dict,
            **kwargs,
        )
    else:
        return self.base_model(
            input_ids = input_ids,
            causal_mask = causal_mask,
            attention_mask = attention_mask,
            inputs_embeds = inputs_embeds,
            labels = labels,
            output_attentions = output_attentions,
            output_hidden_states = output_hidden_states,
            return_dict = return_dict,
            num_logits_to_keep = num_logits_to_keep,
            logits_to_keep = logits_to_keep,
            **kwargs,
        )


def _get_rope_theta(config, default = 10000.0):
    """Get rope_theta from config, handling both transformers 4.x and 5.x."""
    try:
        return config.rope_theta
    except (AttributeError, KeyError):
        pass
    rp = getattr(config, "rope_parameters", None)
    if isinstance(rp, dict):
        return rp.get("rope_theta", default)
    return default


def _rope_scaling_as_dict(rope_scaling):
    """Normalize config.rope_scaling (dict or config object) to a dict; {} on failure."""
    if isinstance(rope_scaling, dict):
        return rope_scaling
    for converter in ("to_dict", "dict"):
        fn = getattr(rope_scaling, converter, None)
        if callable(fn):
            try:
                d = fn()
                if isinstance(d, dict):
                    return d
            except Exception:
                pass
    try:
        return {k: v for k, v in vars(rope_scaling).items() if not k.startswith("_")}
    except TypeError:
        return {}


def _extended_rope_scaling(config, factor):
    """RoPE scaling to extend a model past its native window. Keeps native llama3 as-is
    (linear extension is far worse for long context); everything else gets linear. Returns
    (scaling_or_None, type): None keeps llama3. The linear dict carries rope_theta so
    transformers v5 (which stores it under rope_parameters) keeps the real base, not 10000.
    Only llama3 is preserved because patch_llama_rope_scaling can only rebuild linear/llama3/
    longrope and its longrope branch needs a top-level original_max_position_embeddings."""
    existing = _rope_scaling_as_dict(
        getattr(config, "rope_scaling", None) or getattr(config, "rope_parameters", None) or {}
    )
    existing_type = existing.get("rope_type") or existing.get("type")
    if existing_type == "llama3":
        return None, existing_type
    return {
        "type": "linear",
        "factor": factor,
        "rope_theta": _get_rope_theta(config),
    }, existing_type


def _llama3_inv_freq_from_config(
    config,
    rope_scaling,
    device = "cpu",
):
    """llama3 inv_freq with factors from config; fallback when modeling_rope_utils is missing."""
    base = _get_rope_theta(config, default = 10000.0)
    dim = getattr(config, "head_dim", None)
    if dim is None:
        dim = int(config.hidden_size // config.num_attention_heads)
    inv_freq = 1.0 / (
        base ** (torch.arange(0, dim, 2, dtype = torch.int64, device = device).float() / dim)
    )

    scale_factor = rope_scaling.get("factor", 8.0)
    low_freq_factor = rope_scaling.get("low_freq_factor", 1.0)
    high_freq_factor = rope_scaling.get("high_freq_factor", 4.0)
    old_context_len = rope_scaling.get("original_max_position_embeddings", 8192)

    low_freq_wavelen = old_context_len / low_freq_factor
    high_freq_wavelen = old_context_len / high_freq_factor
    assert low_freq_wavelen != high_freq_wavelen

    # Vectorized meta-llama bands: high freqs kept, low divided by factor, medium blended.
    wavelen = 2 * math.pi / inv_freq
    scaled = torch.where(wavelen > low_freq_wavelen, inv_freq / scale_factor, inv_freq)
    smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
    smoothed = (1 - smooth) * inv_freq / scale_factor + smooth * inv_freq
    is_medium = (wavelen >= high_freq_wavelen) & (wavelen <= low_freq_wavelen)
    return torch.where(is_medium, smoothed, scaled)


def _vanilla_inv_freq_from_config(config, device = "cpu"):
    """Unscaled RoPE inv_freq (rope_type 'default'/None), matching the constructor's fallback."""
    base = _get_rope_theta(config, default = 10000.0)
    dim = getattr(config, "head_dim", None)
    if dim is None:
        dim = int(config.hidden_size // config.num_attention_heads)
    return 1.0 / (base ** (torch.arange(0, dim, 2, dtype = torch.int64, device = device).float() / dim))


def _compute_config_rope_inv_freq(config, rope_scaling):
    """(inv_freq, attention_scaling) per config.rope_scaling via transformers'
    ROPE_INIT_FUNCTIONS, with an inline llama3 fallback; (None, 1.0) on failure."""
    original_rope_scaling = rope_scaling
    rope_scaling = _rope_scaling_as_dict(rope_scaling)
    rope_type = rope_scaling.get("rope_type", None) or rope_scaling.get("type", None)
    # "default"/unset means unscaled RoPE. transformers >=5 reports
    # rope_type="default" for every plain config and dropped "default" from
    # ROPE_INIT_FUNCTIONS, so compute it directly instead of warning per load.
    if rope_type in (None, "default"):
        return _vanilla_inv_freq_from_config(config).to(dtype = torch.float32, device = "cpu"), 1.0
    try:
        from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS

        rope_init_fn = ROPE_INIT_FUNCTIONS[rope_type]
        try:
            inv_freq, attention_scaling = rope_init_fn(config, torch.device("cpu"))
        except Exception:
            # Object-style rope_scaling: retry with a config copy carrying the plain dict.
            if isinstance(original_rope_scaling, dict):
                raise
            import copy as _copy

            config_copy = _copy.copy(config)
            config_copy.rope_scaling = rope_scaling
            inv_freq, attention_scaling = rope_init_fn(config_copy, torch.device("cpu"))
        return inv_freq.to(dtype = torch.float32, device = "cpu"), float(attention_scaling)
    except Exception as exception:
        if rope_type == "llama3":
            try:
                return _llama3_inv_freq_from_config(config, rope_scaling), 1.0
            except Exception:
                pass
        logger.warning_once(
            f"Unsloth: Could not apply RoPE scaling '{rope_type}' from config "
            f"({type(exception).__name__}: {exception}); falling back to unscaled RoPE. "
            "Long-context generation may degrade."
        )
        return None, 1.0


# Solves https://github.com/unslothai/unsloth/issues/168
# Static KV Cache was introduced in 4.38.0, causing training to be much slower.
# Inference can now be CUDAGraphed, but we shall retain the old rotary embeddings.
# https://github.com/huggingface/transformers/pull/27931
# https://github.com/huggingface/transformers/blob/v4.37.2/src/transformers/models/llama/modeling_llama.py
class LlamaRotaryEmbedding(torch.nn.Module):
    # Fixes https://github.com/huggingface/transformers/pull/28837
    # https://github.com/microsoft/DeepSpeed/issues/4932
    # The precision of RoPE buffers is not correct, so we cast to int64.
    def __init__(
        self,
        dim = None,
        max_position_embeddings = 2048,
        base = 10000,
        device = None,
        config = None,  # [TODO] Hack to pass in config - need to remove later
    ):
        super().__init__()
        # cos/sin multiplier (1.0 except yarn / longrope); set before any cache build.
        self.attention_scaling = 1.0
        # Base-class-from-config path (modern transformers): derive inv_freq like
        # transformers so config.rope_scaling is not dropped (#2405). Scaled
        # subclasses are excluded to avoid double-scaling.
        if config is not None:
            # [TODO] Hack to pass in config - need to remove later
            base = _get_rope_theta(config, default = base)
            partial_rotary_factor = (
                config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
            )
            dim = getattr(config, "head_dim", None)
            if dim is None:
                dim = int((config.hidden_size // config.num_attention_heads))
            device = DEVICE_TYPE_TORCH
            max_position_embeddings = config.max_position_embeddings

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        # Kept so the v5 rope repair can rebuild the scaled inv_freq (#2405).
        self._unsloth_rope_config = config
        # Dynamic RoPE we first set it to a max of 4 * 8192 tokens then we iteratively grow this
        self.current_rope_size = min(4 * 8192, self.max_position_embeddings)
        self.multi_gpu_cos_cached = [None] * DEVICE_COUNT
        self.multi_gpu_sin_cached = [None] * DEVICE_COUNT

        inv_freq = self._unsloth_recompute_inv_freq()
        self.register_buffer("inv_freq", inv_freq, persistent = False)

        # Build here to make `torch.jit.trace` work.
        for device_idx in range(DEVICE_COUNT):
            self._set_cos_sin_cache(
                seq_len = self.current_rope_size,
                device = torch.device(device_idx),
                dtype = torch.get_default_dtype(),
            )

        # dummy so that patch_utils doesn't fail for now
        self.cos_cached = torch.empty(
            1, device = get_current_device(), dtype = torch.get_default_dtype()
        )
        self.sin_cached = torch.empty(
            1, device = get_current_device(), dtype = torch.get_default_dtype()
        )

    def _apply_inv_freq_scaling(self, inv_freq):
        """Override to apply custom inv_freq scaling (e.g., extended RoPE)."""
        return inv_freq

    def _unsloth_recompute_inv_freq(self):
        # Config scaling (llama3/yarn) first, else vanilla + subclass scaling.
        # Shared by __init__ and the v5 rope repair so they cannot diverge.
        config = getattr(self, "_unsloth_rope_config", None)
        config_inv_freq = None
        rope_scaling = getattr(config, "rope_scaling", None) if config is not None else None
        if rope_scaling is not None and type(self) is LlamaRotaryEmbedding:
            config_inv_freq, self.attention_scaling = _compute_config_rope_inv_freq(
                config,
                rope_scaling,
            )
        if config_inv_freq is not None:
            return config_inv_freq
        inv_freq = 1.0 / (
            self.base
            ** (torch.arange(0, self.dim, 2, dtype = torch.int64, device = "cpu").float() / self.dim)
        )
        return self._apply_inv_freq_scaling(inv_freq)

    def _apply_time_scaling(self, t):
        """Override to apply custom time scaling (e.g., linear scaling)."""
        return t

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        # Note: on the original Llama codebase, these tensors are created on the target device (and not on CPU) and
        # in FP32. They are applied (multiplied) in FP32 as well.
        self.current_rope_size = seq_len
        t = torch.arange(
            self.current_rope_size, device = self.inv_freq.device, dtype = torch.int64
        ).float()
        t = self._apply_time_scaling(t)

        freqs = torch.outer(t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim = -1)
        # Applied here so attention_scaling survives extend_rope_embedding rebuilds;
        # default 1.0 keeps unscaled paths bit-identical.
        cos = (emb.cos() * self.attention_scaling).to(dtype = dtype, device = device, non_blocking = True)
        sin = (emb.sin() * self.attention_scaling).to(dtype = dtype, device = device, non_blocking = True)
        self.multi_gpu_cos_cached[device.index] = cos
        self.multi_gpu_sin_cached[device.index] = sin
        return cos, sin

    def forward(
        self,
        x,
        position_ids = None,
        seq_len = None,
    ):
        # x: [bs, num_attention_heads, seq_len, head_size]
        if seq_len is not None and seq_len > self.current_rope_size:
            self._set_cos_sin_cache(seq_len = seq_len, device = x.device, dtype = x.dtype)

        device_index = x.device.index
        return (
            self.multi_gpu_cos_cached[device_index][:seq_len],
            self.multi_gpu_sin_cached[device_index][:seq_len],
        )

    def get_cached(
        self,
        seq_len = None,
        device_index = None,
    ):
        if device_index is None:
            device_index = get_current_device()
        return self.multi_gpu_cos_cached[device_index], self.multi_gpu_sin_cached[device_index]

    def extend_rope_embedding(self, x, seq_len):
        if seq_len <= self.current_rope_size:
            return
        # Iteratively grow by increments of 8192
        self.current_rope_size = ((seq_len // 8192) + ((seq_len % 8192) != 0)) * 8192
        for device_idx in range(DEVICE_COUNT):
            self._set_cos_sin_cache(
                self.current_rope_size, device = torch.device(device_idx), dtype = x.dtype
            )


class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
    """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""

    # Fixes https://github.com/huggingface/transformers/pull/28837
    # https://github.com/microsoft/DeepSpeed/issues/4932
    # The precision of RoPE buffers is not correct, so we cast to int64.
    def __init__(
        self,
        dim = None,
        max_position_embeddings = 2048,
        base = 10000,
        device = None,
        scaling_factor = 1.0,
        config = None,  # [TODO] Hack to pass in config - need to remove later
    ):
        self.scaling_factor = scaling_factor
        super().__init__(
            dim = dim,
            max_position_embeddings = max_position_embeddings,
            base = base,
            device = device,
            config = config,
        )

    def _apply_time_scaling(self, t):
        """Apply linear scaling to time indices."""
        return t / self.scaling_factor


# See https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/rotary_embedding.py#L736
# For Llama 3.1
class LlamaExtendedRotaryEmbedding(LlamaRotaryEmbedding):
    def __init__(
        self,
        dim = None,
        max_position_embeddings = 2048,
        base = 10000,
        device = None,
        config = None,  # [TODO] Hack to pass in config - need to remove later
    ):
        super().__init__(
            dim = dim,
            max_position_embeddings = max_position_embeddings,
            base = base,
            device = device,
            config = config,
        )

    # From https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/api/model.py#L41
    def _apply_inv_freq_scaling(self, freqs: torch.Tensor):
        # llama3 factors from config; Llama-3.1 defaults when built without one
        # (legacy codegen path). Hardcoding 8 is wrong for e.g. Llama-3.2 (32).
        # v5 renames rope_scaling -> rope_parameters; read either so the factor
        # survives even if the rope_scaling back-compat shim is dropped.
        config = getattr(self, "_unsloth_rope_config", None)
        rope_scaling = _rope_scaling_as_dict(
            getattr(config, "rope_scaling", None) or getattr(config, "rope_parameters", None) or {}
        )
        scale_factor = rope_scaling.get("factor", 8)
        low_freq_factor = rope_scaling.get("low_freq_factor", 1)
        high_freq_factor = rope_scaling.get("high_freq_factor", 4)
        old_context_len = rope_scaling.get("original_max_position_embeddings", 8192)

        low_freq_wavelen = old_context_len / low_freq_factor
        high_freq_wavelen = old_context_len / high_freq_factor
        new_freqs = []
        for freq in freqs:
            wavelen = 2 * math.pi / freq
            if wavelen < high_freq_wavelen:
                new_freqs.append(freq)
            elif wavelen > low_freq_wavelen:
                new_freqs.append(freq / scale_factor)
            else:
                assert low_freq_wavelen != high_freq_wavelen
                smooth = (old_context_len / wavelen - low_freq_factor) / (
                    high_freq_factor - low_freq_factor
                )
                new_freqs.append((1 - smooth) * freq / scale_factor + smooth * freq)
        return torch.tensor(new_freqs, dtype = freqs.dtype, device = freqs.device)


class LongRopeRotaryEmbedding(torch.nn.Module):
    # For Phi 3.5 128K https://huggingface.co/microsoft/Phi-3.5-mini-instruct/blob/main/modeling_phi3.py
    def __init__(
        self,
        dim = None,
        max_position_embeddings = 131072,
        original_max_position_embeddings = 4096,
        base = 10000,
        short_factor = None,
        long_factor = None,
        device = None,
        config = None,  # [TODO] Hack to pass in config - need to remove later
    ):
        super().__init__()
        assert short_factor is not None
        assert long_factor is not None
        assert type(original_max_position_embeddings) is int

        if config is not None:
            # [TODO] Hack to pass in config - need to remove later
            base = _get_rope_theta(config, default = base)
            partial_rotary_factor = (
                config.partial_rotary_factor if hasattr(config, "partial_rotary_factor") else 1.0
            )
            dim = int((config.hidden_size // config.num_attention_heads))
            device = DEVICE_TYPE_TORCH
            max_position_embeddings = config.max_position_embeddings

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.original_max_position_embeddings = original_max_position_embeddings
        self.base = base
        # Dynamic RoPE we first set it to a max of 4 * 8192 tokens then we iteratively grow this
        self.current_rope_size = min(original_max_position_embeddings, self.max_position_embeddings)
        self.multi_gpu_short_cos_cached = [None] * DEVICE_COUNT
        self.multi_gpu_short_sin_cached = [None] * DEVICE_COUNT
        self.multi_gpu_long_cos_cached = [None] * DEVICE_COUNT
        self.multi_gpu_long_sin_cached = [None] * DEVICE_COUNT

        # Long RoPE similar to RoPE except short sequences have 1 cos / sin
        # and long sequences have another cos / sin
        inv_freq_shape = (
            torch.arange(0, self.dim, 2, dtype = torch.int64, device = "cpu").float() / self.dim
        )
        short_factor = torch.tensor(short_factor, device = "cpu", dtype = torch.float32)
        long_factor = torch.tensor(long_factor, device = "cpu", dtype = torch.float32)
        short_inv_freq = 1.0 / (short_factor * self.base**inv_freq_shape)
        long_inv_freq = 1.0 / (long_factor * self.base**inv_freq_shape)

        # Phi-3 Scale factor
        scale = self.max_position_embeddings / self.original_max_position_embeddings
        if scale <= 1.0:
            scaling_factor = 1.0
        else:
            scaling_factor = math.sqrt(
                1 + math.log(scale) / math.log(self.original_max_position_embeddings)
            )
        self.scaling_factor = scaling_factor

        # Short and long inv_freq
        self.register_buffer("short_inv_freq", short_inv_freq, persistent = False)
        self.register_buffer("long_inv_freq", long_inv_freq, persistent = False)

        # Build here to make `torch.jit.trace` work.
        # Initialize short sequences cache for all devices
        dtype = torch.bfloat16 if is_bfloat16_supported() else torch.float16
        t = torch.arange(
            original_max_position_embeddings,
            device = self.short_inv_freq.device,
            dtype = torch.int64,
        ).float()
        freqs = torch.outer(t, self.short_inv_freq)
        emb = torch.cat((freqs, freqs), dim = -1)

        for device_idx in range(DEVICE_COUNT):
            device_obj = torch.device(device_idx)
            cos_cached = (emb.cos() * self.scaling_factor).to(
                dtype = dtype, device = device_obj, non_blocking = True
            )
            sin_cached = (emb.sin() * self.scaling_factor).to(
                dtype = dtype, device = device_obj, non_blocking = True
            )
            self.multi_gpu_short_cos_cached[device_idx] = cos_cached
            self.multi_gpu_short_sin_cached[device_idx] = sin_cached

        # dummy so that patch_utils doesn't fail for now
        self.short_cos_cached = torch.empty(
            1, device = get_current_device(), dtype = torch.get_default_dtype()
        )
        self.short_sin_cached = torch.empty(
            1, device = get_current_device(), dtype = torch.get_default_dtype()
        )
        self.long_cos_cached = torch.empty(
            1, device = get_current_device(), dtype = torch.get_default_dtype()
        )
        self.long_sin_cached = torch.empty(
            1, device = get_current_device(), dtype = torch.get_default_dtype()
        )

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        # Note: on the original Llama codebase, these tensors are created on the target device (and not on CPU) and
        # in FP32. They are applied (multiplied) in FP32 as well.
        self.current_rope_size = seq_len

        t = torch.arange(
            self.current_rope_size, device = self.long_inv_freq.device, dtype = torch.int64
        ).float()
        # Long sequences
        freqs = torch.outer(t, self.long_inv_freq)
        emb = torch.cat((freqs, freqs), dim = -1)
        cos_cached = (emb.cos() * self.scaling_factor).to(
            dtype = dtype, device = device, non_blocking = True
        )
        sin_cached = (emb.sin() * self.scaling_factor).to(
            dtype = dtype, device = device, non_blocking = True
        )
        self.multi_gpu_long_cos_cached[device.index] = cos_cached
        self.multi_gpu_long_sin_cached[device.index] = sin_cached
        return cos_cached, sin_cached

    def forward(
        self,
        x,
        position_ids = None,
        seq_len = None,
    ):
        # x: [bs, num_attention_heads, seq_len, head_size]
        if seq_len is not None and seq_len > self.current_rope_size:
            self._set_cos_sin_cache(seq_len = seq_len, device = x.device, dtype = x.dtype)

        device_index = x.device.index

        if seq_len is not None and seq_len < self.original_max_position_embeddings:
            return (
                self.multi_gpu_short_cos_cached[device_index][:seq_len],
                self.multi_gpu_short_sin_cached[device_index][:seq_len],
            )
        else:
            return (
                self.multi_gpu_long_cos_cached[device_index][:seq_len],
                self.multi_gpu_long_sin_cached[device_index][:seq_len],
            )

    def get_cached(
        self,
        seq_len = None,
        device_index = None,
    ):
        if device_index is None:
            device_index = get_current_device()
        if seq_len is not None and seq_len < self.original_max_position_embeddings:
            return self.multi_gpu_short_cos_cached[device_index], self.multi_gpu_short_sin_cached[
                device_index
            ]
        return self.multi_gpu_long_cos_cached[device_index], self.multi_gpu_long_sin_cached[
            device_index
        ]

    def extend_rope_embedding(self, x, seq_len):
        if seq_len <= self.current_rope_size:
            return
        # Iteratively grow by increments of 8192
        self.current_rope_size = ((seq_len // 8192) + ((seq_len % 8192) != 0)) * 8192
        for device_idx in range(DEVICE_COUNT):
            self._set_cos_sin_cache(
                self.current_rope_size, device = torch.device(device_idx), dtype = x.dtype
            )


def unsloth_fast_generate(self, *args, **kwargs):
    # Restore training mode after generation if we started in it
    restore_training_mode = self.training
    # Snapshot the real GC mode (e.g. "unsloth") before for_inference clears it,
    # so the restore preserves it rather than collapsing to a plain bool.
    use_gradient_checkpointing = next(
        (v for v in (getattr(m, "gradient_checkpointing", False) for m in self.modules()) if v),
        False,
    )

    FastLlamaModel.for_inference(self)

    # Unpack BatchEncoding passed as input_ids (old notebooks do
    # generate(input_ids=tokenizer(...))). v5 generate() calls .shape on it and
    # crashes; unpack into separate kwargs so v4 and v5 both work.
    _maybe_encoding = kwargs.get("input_ids", None)
    if (
        _maybe_encoding is not None
        and not isinstance(_maybe_encoding, torch.Tensor)
        and hasattr(_maybe_encoding, "items")
    ):
        batch_data = kwargs.pop("input_ids")
        for key, val in batch_data.items():
            kwargs.setdefault(key, val)

    dtype = _get_dtype(dtype_from_config(self.config))

    if hasattr(self, "config") and hasattr(self.config, "max_position_embeddings"):
        if "input_ids" in kwargs and kwargs["input_ids"] is not None and "max_new_tokens" in kwargs:
            _ids = kwargs["input_ids"]
            if hasattr(_ids, "shape") and (
                _ids.shape[-1] + kwargs["max_new_tokens"] > self.config.max_position_embeddings
            ):
                raise ValueError(
                    f"Unsloth: input length {_ids.shape[-1]} + max_new_tokens {kwargs['max_new_tokens']} exceeds the maximum sequence length of {self.config.max_position_embeddings}!\n"
                    "You will need to do long context extension by increasing the `max_seq_length` in `FastLanguageModel.from_pretrained`."
                )

    # Must patch accelerate for Xformers
    # if accelerate_new_send_to_device is not None:
    #     import accelerate.utils.operations
    #     accelerate.utils.operations.send_to_device = accelerate_new_send_to_device
    # pass

    # For newer HF
    kwargs["cache_implementation"] = "dynamic"
    # transformers 4.50 renamed num_logits_to_keep -> logits_to_keep; pop both,
    # re-emit under the spelling forward() accepts.
    _provided_num = kwargs.pop("num_logits_to_keep", None)
    _provided_logits = kwargs.pop("logits_to_keep", None)
    _provided = _provided_logits if _provided_logits is not None else _provided_num
    try:
        _fwd_params = inspect.signature(self.forward).parameters
        _has_new = "logits_to_keep" in _fwd_params
        _has_old = "num_logits_to_keep" in _fwd_params
    except (TypeError, ValueError):
        # Opaque forward: keep the caller's spelling, default to new.
        _has_old = _provided_num is not None and _provided_logits is None
        _has_new = not _has_old
    if _has_new:
        kwargs["logits_to_keep"] = _provided if _provided is not None else 1
    elif _has_old:
        kwargs["num_logits_to_keep"] = _provided if _provided is not None else 1

    # Remove token_type_ids
    kwargs.pop("token_type_ids", None)

    # Check pad_token
    model_eos_token_id = getattr(self.config, "eos_token_id", None)
    if model_eos_token_id is not None and hasattr(model_eos_token_id, "__iter__"):
        model_eos_token_id = model_eos_token_id[0]

    kwargs["pad_token_id"] = kwargs.pop("pad_token_id", model_eos_token_id)

    # Mixed precision autocast
    with (
        _get_inference_mode_context_manager(self),
        torch.autocast(device_type = DEVICE_TYPE_TORCH, dtype = dtype),
    ):
        output = self._old_generate(*args, **kwargs)

    # Return accelerate back
    # if accelerate_new_send_to_device is not None:
    #     accelerate.utils.operations.send_to_device = accelerate_old_send_to_device
    # pass

    if restore_training_mode:
        FastLlamaModel.for_training(
            self,
            use_gradient_checkpointing = use_gradient_checkpointing,
        )

    return output


class FastLlamaModel:
    @staticmethod
    def _prepare_for_qat(model, qat_scheme):
        model = _prepare_model_for_qat(model, qat_scheme)
        return model

    @staticmethod
    def pre_patch():
        init_name, function = patch_llama_rope_scaling(
            model_name = "llama",
            rope_module = LlamaRotaryEmbedding,
            scaled_rope_module = LlamaLinearScalingRotaryEmbedding,
            extended_rope_module = LlamaExtendedRotaryEmbedding,
            attention_module = LlamaAttention,
            longrope_module = LongRopeRotaryEmbedding,
        )
        if init_name is not None:
            exec(function, globals())
            LlamaAttention.__init__ = eval(init_name)
        LlamaAttention.forward = LlamaAttention_fast_forward
        LlamaSdpaAttention.forward = LlamaAttention_fast_forward
        LlamaFlashAttention2.forward = LlamaAttention_fast_forward
        LlamaDecoderLayer.forward = LlamaDecoderLayer_fast_forward
        LlamaModel.forward = LlamaModel_fast_forward
        LlamaForCausalLM.forward = CausalLM_fast_forward(LlamaModel_fast_forward_inference)
        PeftModelForCausalLM.forward = PeftModel_fast_forward
        fix_prepare_inputs_for_generation(LlamaForCausalLM)

        # Solves https://github.com/unslothai/unsloth/issues/168
        # Static KV Cache was introduced in 4.38.0, causing training to be much slower.
        # Inference can now be CUDAGraphed, but we shall retain the old rotary embeddings.
        # https://github.com/huggingface/transformers/pull/27931
        # https://github.com/huggingface/transformers/blob/v4.37.2/src/transformers/models/llama/modeling_llama.py
        import transformers.models.llama.modeling_llama

        transformers.models.llama.modeling_llama.LlamaRotaryEmbedding = LlamaRotaryEmbedding
        transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding = (
            LlamaLinearScalingRotaryEmbedding
        )
        return

    @staticmethod
    def from_pretrained(
        model_name = "unsloth/llama-3-8b-bnb-4bit",
        max_seq_length = None,
        dtype = None,
        load_in_4bit = True,
        token = None,
        device_map = "sequential",
        rope_scaling = None,
        fix_tokenizer = True,
        model_patcher = None,
        tokenizer_name = None,
        trust_remote_code = False,
        revision = None,
        fast_inference = False,  # uses vLLM
        gpu_memory_utilization = 0.5,
        float8_kv_cache = False,
        random_state = 3407,
        max_lora_rank = 16,
        disable_log_stats = False,
        unsloth_vllm_standby = False,
        num_labels = None,
        qat_scheme = None,
        load_in_fp8 = False,  # fp8 LoRA (True, False, 'block')
        **kwargs,
    ):
        os.environ["UNSLOTH_USE_NEW_MODEL"] = "0"
        if trust_remote_code:
            if fast_inference:
                raise NotImplementedError(
                    "Unsloth: Fast inference does not support `trust_remote_code` yet."
                )
            print(
                "Unsloth: WARNING `trust_remote_code` is True.\n"
                "Are you certain you want to do remote code execution?"
            )
        if fast_inference:
            if not is_vLLM_available():
                print("Unsloth: vLLM is not installed! Will use Unsloth inference!")
                fast_inference = False
            if DEVICE_TYPE == "cuda":
                major_version, minor_version = torch.cuda.get_device_capability()
                if major_version < 7:
                    print(
                        "Unsloth: vLLM does not work on older GPUs - will switch to Unsloth inference!"
                    )
                    fast_inference = False
            elif DEVICE_TYPE == "hip":
                fast_inference = True
            if unsloth_vllm_standby and os.environ.get("UNSLOTH_VLLM_STANDBY", "0") == "0":
                raise RuntimeError(
                    "Unsloth: `unsloth_vllm_standby` is True, but  environment variable `UNSLOTH_VLLM_STANDBY` is not set to 1!"
                )

        token = hf_login(token)
        if model_patcher is None:
            model_patcher = FastLlamaModel
        SUPPORTS_BFLOAT16 = is_bfloat16_supported()

        if DEVICE_TYPE == "cuda":
            gpu_stats = torch.cuda.get_device_properties(0)
            gpu_stats_name = (
                gpu_stats.name + ". " if gpu_stats.name != "" else "NVIDIA GPU Device. "
            )
            gpu_version = torch.version.cuda
            gpu_stats_snippet = (
                f"CUDA: {gpu_stats.major}.{gpu_stats.minor}. CUDA Toolkit: {gpu_version}."
            )
            try:
                vllm_version = f" vLLM: {importlib_version('vllm')}."
            except:
                vllm_version = ""
        elif DEVICE_TYPE == "hip":
            gpu_stats = torch.cuda.get_device_properties(0)
            gpu_stats_name = resolve_hip_gpu_stats_name(gpu_stats)
            gpu_version = torch.version.hip
            gpu_stats_snippet = f"ROCm Toolkit: {gpu_version}."
            try:
                vllm_version = f" vLLM: {importlib_version('vllm')}."
            except:
                vllm_version = ""
        elif DEVICE_TYPE == "xpu":
            gpu_stats = torch.xpu.get_device_properties(0)
            gpu_stats_name = gpu_stats.name + ". " if gpu_stats.name != "" else "Intel XPU Device. "
            gpu_version = torch.version.xpu
            gpu_stats_snippet = f"Intel Toolkit: {gpu_version}."
            try:
                vllm_version = f" vLLM: {importlib_version('vllm')}."
            except:
                vllm_version = ""
        else:
            raise ValueError(f"Unsloth: Unsupported device type: {DEVICE_TYPE}")

        max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)

        statistics = (
            f"==((====))==  Unsloth {__version__}: Fast {model_patcher.__name__[4:-5]} patching. Transformers: {transformers_version}.{vllm_version}\n"
            f"   {chr(92)}{chr(92)}   /|    {gpu_stats_name}Num GPUs = {DEVICE_COUNT}. Max memory: {max_memory} GB. Platform: {platform_system}.\n"
            f"O^O/ {chr(92)}_/ {chr(92)}    Torch: {torch.__version__}. {gpu_stats_snippet} Triton: {triton_version}\n"
            f"{chr(92)}        /    Bfloat16 = {str(SUPPORTS_BFLOAT16).upper()}. FA [Xformers = {xformers_version}. FA2 = {HAS_FLASH_ATTENTION}]\n"
            f' "-____-"     Free license: http://github.com/unslothai/unsloth'
        )

        print(statistics)

        # Warn about fast transfers
        if "HF_HUB_ENABLE_HF_TRANSFER" in os.environ:
            old_hf_transfer = os.environ["HF_HUB_ENABLE_HF_TRANSFER"]
            if old_hf_transfer in ("False", "false"):
                old_hf_transfer = "0"
            if old_hf_transfer in ("True", "true"):
                old_hf_transfer = "1"
        else:
            old_hf_transfer = "0"
        if old_hf_transfer == "1":
            print(
                "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!"
            )
        if old_hf_transfer != "0":
            os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"

        model_patcher.pre_patch()
        # For debugging - we use a download counter to see if environments are not breaking or if HF is down
        get_statistics(kwargs.get("local_files_only", False))

        if dtype is None:
            dtype = torch.float16 if not SUPPORTS_BFLOAT16 else torch.bfloat16
        elif dtype == torch.bfloat16 and not SUPPORTS_BFLOAT16:
            logger.warning_once("Device does not support bfloat16. Will change to float16.")
            dtype = torch.float16
        # elif dtype == torch.float16 and SUPPORTS_BFLOAT16:
        #     logger.warning_once("Device supports bfloat16 but you selected float16. Will change to bfloat16.")
        #     dtype = torch.bfloat16

        assert dtype == torch.float16 or dtype == torch.bfloat16 or dtype == torch.float32

        # RoPE Scaling
        # Respect a user-provided config so it is the single config object used
        # everywhere below; otherwise HF would receive it again through **kwargs
        # alongside our own config= and fail with a duplicate-kwarg TypeError.
        user_config = kwargs.pop("config", None)
        if user_config is not None:
            model_config = user_config
            # model_name may have been remapped to a prequantized repo whose
            # checkpoint needs its quantization_config; graft it onto the user
            # config or the 4bit weights load without their quant state.
            if getattr(model_config, "quantization_config", None) is None:
                _checkpoint_config = AutoConfig.from_pretrained(
                    model_name,
                    token = token,
                    attn_implementation = "sdpa",
                )
                _checkpoint_quant = getattr(_checkpoint_config, "quantization_config", None)
                if _checkpoint_quant is not None:
                    model_config.quantization_config = _checkpoint_quant
        else:
            model_config = AutoConfig.from_pretrained(
                model_name,
                token = token,
                attn_implementation = "sdpa",
            )
        model_config.model_name = model_name
        model_max_seq_length = model_config.max_position_embeddings

        verify_fp8_support_if_applicable(model_config)

        # Check if RoPE Scaling is even allowed
        model_function = MODEL_FOR_CAUSAL_LM_MAPPING[model_config.__class__]
        IS_FALCON_H1 = model_config.model_type.startswith("falcon_h1")

        preferred_attn_impl = resolve_attention_implementation(model_function, model_config)

        # Prefetch the repo (killable child) so the weight load is a cache hit. Runs after the
        # AutoConfig/model-class check so an unsupported repo fails on its small config fetch. No
        # revision: the load resolves model_name (maybe a remapped prequant repo) on its default branch.
        _prefetched = maybe_prefetch_hf_snapshot(
            model_name,
            token = token,
            cache_dir = kwargs.get("cache_dir"),
            local_files_only = kwargs.get("local_files_only", False),
            # Skip the warm only for a real vLLM load; a num_labels classification load still goes
            # in-process below, so it must be warmed even under fast_inference.
            fast_inference = fast_inference and num_labels is None,
            subfolder = kwargs.get("subfolder"),
            force_download = kwargs.get("force_download", False),
            use_safetensors = kwargs.get("use_safetensors"),
            from_tf = kwargs.get("from_tf", False),
            from_flax = kwargs.get("from_flax", False),
            # Bare load reads only ROOT weights; skip subdir weights. Ignored when a subfolder is set.
            weights_at_root = True,
            variant = kwargs.get("variant"),  # forward so the warm keeps the variant .bin
            gguf_file = kwargs.get(
                "gguf_file"
            ),  # forward so the warm fetches the GGUF (else ignored)
        )
        # Child did the forced download; clear the flag so the load reuses the warm cache.
        if _prefetched and kwargs.get("force_download", False):
            kwargs["force_download"] = False

        # Tokenizer always loads in-process. Resolve the cache_dir the tokenizer load will actually
        # use, mirroring load_correct_tokenizer: without an explicit cache_dir, Colab/Kaggle route to
        # a special tokenizer cache (huggingface_tokenizers_cache / Kaggle tmp), NOT the HF-default
        # cache the base snapshot warmed. So the base warm does not cover the tokenizer there.
        from ..tokenizer_utils import (
            IS_COLAB_ENVIRONMENT,
            IS_KAGGLE_ENVIRONMENT,
            KAGGLE_TMP,
        )

        _tokenizer_repo = (
            tokenizer_name if (isinstance(tokenizer_name, str) and tokenizer_name) else model_name
        )
        _tokenizer_cache_dir = kwargs.get("cache_dir")
        if _tokenizer_cache_dir is None:
            if IS_COLAB_ENVIRONMENT:
                _tokenizer_cache_dir = "huggingface_tokenizers_cache"
            elif IS_KAGGLE_ENVIRONMENT:
                _tokenizer_cache_dir = os.path.join(KAGGLE_TMP, "huggingface_tokenizers_cache")
        # Warm the tokenizer repo into the cache the load will use whenever the base warm did not
        # cover it: a distinct tokenizer repo, fast_inference (base warm skipped), or a tokenizer
        # cache_dir that differs from the base-warm cache_dir (Colab/Kaggle special cache).
        _warm_tokenizer_repo = (
            isinstance(_tokenizer_repo, str)
            and bool(_tokenizer_repo)
            and (
                _tokenizer_repo != model_name
                or fast_inference
                or _tokenizer_cache_dir != kwargs.get("cache_dir")
            )
        )
        if _warm_tokenizer_repo:
            maybe_prefetch_hf_snapshot(
                _tokenizer_repo,
                token = token,
                cache_dir = _tokenizer_cache_dir,
                local_files_only = kwargs.get("local_files_only", False),
                tokenizer_only = True,
            )

        has_rope_scaling = False
        try:
            with open(inspect.getfile(model_function), "r", encoding = "utf-8") as file:
                has_rope_scaling = "self.config.rope_scaling" in file.read()
        except:
            pass
        has_rope_scaling = True

        # If max_seq_length is not specified, use maximum from config
        if max_seq_length is None:
            max_seq_length = model_max_seq_length

        if (rope_scaling is None) and (max_seq_length > model_max_seq_length):
            factor = max_seq_length / model_max_seq_length

            if fast_inference:
                raise NotImplementedError(
                    "Unsloth: Fast inference does not yet work with RoPE Scaling."
                )

            linear_scaling, native_type = _extended_rope_scaling(model_config, factor)
            if linear_scaling is not None:
                logger.warning_once(
                    f"Unsloth: {model_name} can only handle sequence lengths of at most "
                    f"{model_max_seq_length}.\nBut with kaiokendev's RoPE scaling of "
                    f"{round(factor, 3)}, it can be magically be extended to "
                    f"{max_seq_length}!"
                )
                if not has_rope_scaling:
                    raise RuntimeError(
                        f"However, {model_name} doesn't support RoPE Scaling!\n"
                        "Please file a feature request at https://github.com/unslothai/unsloth."
                    )
                kwargs["rope_scaling"] = linear_scaling
            else:
                # Native llama3 scaling already handles long context; just widen the window.
                logger.warning_once(
                    f"Unsloth: extending {model_name} to {max_seq_length} using its native "
                    f"{native_type} RoPE scaling."
                )

        from .loader_utils import (
            check_and_disable_bitsandbytes_loading,
            sync_unsloth_model_name_bnb_flags,
        )
        from unsloth_zoo.utils import get_quant_type

        # Extract load_in_8bit from kwargs if provided
        load_in_8bit = kwargs.get("load_in_8bit", False)

        # Check and disable bitsandbytes loading if model has non-bitsandbytes quantization
        load_in_4bit, load_in_8bit, _ckpt_quant_method = check_and_disable_bitsandbytes_loading(
            model_config, load_in_4bit = load_in_4bit, load_in_8bit = load_in_8bit
        )
        # Correct UNSLOTH_MODEL_NAME's bnb tokens now that the effective bnb state is known
        # (the per-load env was built before remap/disable). gpt-oss only; no-op otherwise.
        sync_unsloth_model_name_bnb_flags(load_in_4bit, load_in_8bit)

        bnb_config = None
        _ckpt_qcfg = getattr(model_config, "quantization_config", None)

        if load_in_4bit:
            llm_int8_skip_modules = SKIP_QUANTIZATION_MODULES.copy()
            if IS_FALCON_H1:
                # we cannot quantize out_proj layer due to mamba kernels: https://github.com/tiiuae/Falcon-H1/issues/13#issuecomment-2918671274
                llm_int8_skip_modules.append("out_proj")
            bnb_config = BitsAndBytesConfig(
                load_in_4bit = True,
                bnb_4bit_use_double_quant = True,
                bnb_4bit_quant_type = "nf4",
                bnb_4bit_compute_dtype = dtype,
                llm_int8_skip_modules = llm_int8_skip_modules,
            )
            # Pre-quantized checkpoints (e.g. unsloth/Qwen3-4B-bnb-4bit) use the
            # quantization_config baked into config.json, ignoring our runtime
            # BitsAndBytesConfig. Merge our skip list into the bundled config so
            # task heads like `score` stay in compute dtype. See unslothai/unsloth#5027.
            if _ckpt_quant_method == "bitsandbytes" and _ckpt_qcfg is not None:
                if isinstance(_ckpt_qcfg, dict):
                    _ckpt_skip = list(_ckpt_qcfg.get("llm_int8_skip_modules") or [])
                    for _m in llm_int8_skip_modules:
                        if _m not in _ckpt_skip:
                            _ckpt_skip.append(_m)
                    _ckpt_qcfg["llm_int8_skip_modules"] = _ckpt_skip
                else:
                    _ckpt_skip = list(getattr(_ckpt_qcfg, "llm_int8_skip_modules", None) or [])
                    for _m in llm_int8_skip_modules:
                        if _m not in _ckpt_skip:
                            _ckpt_skip.append(_m)
                    try:
                        _ckpt_qcfg.llm_int8_skip_modules = _ckpt_skip
                    except Exception:
                        pass

        # https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/discussions/12
        # RoPE Scaling's max_position_embeddings must be updated
        max_position_embeddings = max(max_seq_length, model_max_seq_length)
        kwargs.pop("attn_implementation", None)  # No need since we auto call it

        # Cannot be None, since HF now checks for the config
        if load_in_4bit:
            kwargs["quantization_config"] = bnb_config

        kwargs = add_dtype_kwargs(dtype, kwargs)

        raise_handler = RaiseUninitialized()
        try:
            if num_labels is not None:
                # Transformers 5.x @strict config classes reject unexpected kwargs
                # like num_labels and max_position_embeddings. Set on the config
                # object directly and pass config= instead.
                set_task_config_attr(model_config, "num_labels", num_labels)
                if max_position_embeddings is not None:
                    model_config.max_position_embeddings = max_position_embeddings
                # Pop config-level attrs that would be rejected by @strict model init
                for _cfg_key in ("id2label", "label2id", "rope_scaling"):
                    _cfg_val = kwargs.pop(_cfg_key, None)
                    if _cfg_val is not None:
                        if _cfg_key in ("id2label", "label2id"):
                            set_task_config_attr(model_config, _cfg_key, _cfg_val)
                        else:
                            setattr(model_config, _cfg_key, _cfg_val)
                model = AutoModelForSequenceClassification.from_pretrained(
                    model_name,
                    config = model_config,
                    device_map = device_map,
                    # torch_dtype             = dtype, # transformers changed torch_dtype to dtype
                    # quantization_config     = bnb_config,
                    token = token,
                    trust_remote_code = trust_remote_code,
                    attn_implementation = preferred_attn_impl,
                    **kwargs,
                )
                # Defensive: ensure the task head is in a floating dtype, guarding
                # against any path leaving it as integer storage. See unslothai/unsloth#5027.
                for _head_name in ("score", "classifier", "qa_outputs"):
                    _head = getattr(model, _head_name, None)
                    if (
                        _head is not None
                        and hasattr(_head, "weight")
                        and not _head.weight.is_floating_point()
                    ):
                        _head.to(dtype)
                # Attach dispatch hooks for bnb multi-device loads.
                from unsloth.models.vision import _attach_bnb_multidevice_hooks

                _attach_bnb_multidevice_hooks(
                    model,
                    load_in_4bit = load_in_4bit,
                    load_in_8bit = kwargs.get("load_in_8bit", False),
                    offload_embedding = False,
                    fast_inference = fast_inference,
                )
                # Re-apply block-fp8 weight_scale_inv tensors transformers dropped on load (#6200).
                _restore_dropped_fp8_scales(
                    model,
                    model_name,
                    local_files_only = kwargs.get("local_files_only", False),
                    token = token,
                    # Weights load from the default branch (revision not forwarded), so read scales from there too.
                    revision = None,
                    subfolder = kwargs.get("subfolder"),
                    cache_dir = kwargs.get("cache_dir"),
                    variant = kwargs.get("variant"),
                )
            elif not fast_inference:
                if user_config is not None:
                    # Transformers 5.x @strict model init rejects extra kwargs next
                    # to config=; set the override on the config and pass the single
                    # config object through so user overrides reach the actual load.
                    if max_position_embeddings is not None:
                        model_config.max_position_embeddings = max_position_embeddings
                    model = AutoModelForCausalLM.from_pretrained(
                        model_name,
                        config = model_config,
                        device_map = device_map,
                        token = token,
                        trust_remote_code = trust_remote_code,
                        attn_implementation = preferred_attn_impl,
                        **kwargs,
                    )
                else:
                    model = AutoModelForCausalLM.from_pretrained(
                        model_name,
                        device_map = device_map,
                        # torch_dtype             = dtype, # transformers changed torch_dtype to dtype
                        # quantization_config     = bnb_config,
                        token = token,
                        max_position_embeddings = max_position_embeddings,
                        trust_remote_code = trust_remote_code,
                        attn_implementation = preferred_attn_impl,
                        **kwargs,
                    )
                # Attach dispatch hooks for bnb multi-device loads.
                from unsloth.models.vision import _attach_bnb_multidevice_hooks

                _attach_bnb_multidevice_hooks(
                    model,
                    load_in_4bit = load_in_4bit,
                    load_in_8bit = kwargs.get("load_in_8bit", False),
                    offload_embedding = False,
                    fast_inference = False,
                )
                # Re-apply block-fp8 weight_scale_inv tensors transformers dropped on load (#6200).
                _restore_dropped_fp8_scales(
                    model,
                    model_name,
                    local_files_only = kwargs.get("local_files_only", False),
                    token = token,
                    # Weights load from the default branch (revision not forwarded), so read scales from there too.
                    revision = None,
                    subfolder = kwargs.get("subfolder"),
                    cache_dir = kwargs.get("cache_dir"),
                    variant = kwargs.get("variant"),
                )
                model.fast_generate = make_fast_generate_wrapper(model.generate)
                model.fast_generate_batches = None
            else:
                from unsloth_zoo.vllm_utils import (
                    load_vllm,
                    get_vllm_state_dict,
                    convert_vllm_to_huggingface,
                    generate_batches,
                )

                fp8_mode = None
                if load_in_fp8 != False:
                    fp8_mode = _get_fp8_mode_and_check_settings(
                        load_in_fp8,
                        fast_inference,
                    )

                allowed_args = inspect.getfullargspec(load_vllm).args
                load_vllm_kwargs = dict(
                    model_name = model_name,
                    config = model_config,
                    gpu_memory_utilization = gpu_memory_utilization,
                    max_seq_length = max_seq_length,
                    dtype = dtype,
                    float8_kv_cache = float8_kv_cache,
                    enable_lora = True,
                    max_lora_rank = max_lora_rank,
                    disable_log_stats = disable_log_stats,
                    use_bitsandbytes = load_in_4bit,
                    unsloth_vllm_standby = unsloth_vllm_standby,
                    fp8_mode = fp8_mode,
                )
                for allowed_arg in allowed_args:
                    if allowed_arg not in load_vllm_kwargs and allowed_arg in kwargs:
                        load_vllm_kwargs[allowed_arg] = kwargs[allowed_arg]
                pass

                # Load vLLM first
                llm = load_vllm(**load_vllm_kwargs)

                # Convert to HF format
                _, quant_state_dict = get_vllm_state_dict(
                    llm,
                    config = model_config,
                    load_in_fp8 = load_in_fp8,
                )
                model = convert_vllm_to_huggingface(
                    quant_state_dict, model_config, dtype, bnb_config
                )
                model.vllm_engine = llm
                llm.shared_weights = True
                model.fast_generate = model.vllm_engine.generate
                model.fast_generate_batches = functools.partial(generate_batches, model.vllm_engine)
        finally:
            raise_handler.remove()
            # Return old flag
            os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = old_hf_transfer

        # Counteract saved tokenizers
        tokenizer_name = model_name if tokenizer_name is None else tokenizer_name
        # Route the tokenizer load to the custom cache_dir the prefetch warmed.
        _tokenizer_cache_kwargs = {}
        if kwargs.get("cache_dir") is not None:
            _tokenizer_cache_kwargs["cache_dir"] = kwargs["cache_dir"]
        tokenizer = load_correct_tokenizer(
            tokenizer_name = tokenizer_name,
            model_max_length = max_position_embeddings,
            padding_side = "right",
            token = token,
            trust_remote_code = trust_remote_code,
            fix_tokenizer = fix_tokenizer,
            **_tokenizer_cache_kwargs,
        )

        model, tokenizer = patch_tokenizer(model, tokenizer)
        model, tokenizer = model_patcher.post_patch(model, tokenizer, correct_dtype = dtype)

        # Patch up QKV / O and MLP
        for idx, layer in enumerate(model.model.layers):
            layer.self_attn.apply_qkv = original_apply_qkv
            layer.self_attn.apply_o = original_apply_o

        # Patch Trainer
        from transformers.trainer import Trainer

        try:
            if Trainer._inner_training_loop.__name__ != "_fast_inner_training_loop":
                inner_training_loop = inspect.getsource(Trainer._inner_training_loop)
                Trainer._original_training_loop = inner_training_loop
            else:
                inner_training_loop = Trainer._original_training_loop
        except:
            raise RuntimeError("Unsloth: Unsuccessfully patched inner_training_loop")

        import transformers.trainer

        items_in_trainer = dir(transformers.trainer)
        good_items = []
        for item in items_in_trainer:
            if item in inner_training_loop:
                good_items.append(item)
        exec(
            "from transformers.trainer import (" + ", ".join(x for x in good_items) + ")",
            globals(),
        )

        start = re.search(r"logger\.info\([\"\'].+?Running training", inner_training_loop).span(0)[
            0
        ]
        end = inner_training_loop.find("\n\n", start)
        original_debug = inner_training_loop[start:end]
        spaces = re.search(r"\n([\s\t]{1,})", original_debug).group(0)[1:]
        front_spaces = re.match(r"([\s\t]{1,})", inner_training_loop).group(0)

        # Cannot use \\ since it will cause a SyntaxWarning in Python 3.12
        # Instead use chr(92) == \\
        debug_info = """debug_info = \\
        f"==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = {len(set(p.device for p in model.parameters()))}\\n"\\
        f"   {chr(92)}{chr(92)}   /|    Num examples = {num_examples:,} | Num Epochs = {num_train_epochs:,} | Total steps = {max_steps:,}\\n"\\
        f"O^O/ {chr(92)}_/ {chr(92)}    Batch size per device = {self._train_batch_size:,} | Gradient accumulation steps = {args.gradient_accumulation_steps}\\n"\\
        f"{chr(92)}        /    Data Parallel GPUs = {args.world_size} | Total batch size ({self._train_batch_size} x {args.gradient_accumulation_steps} x {args.world_size}) = {total_train_batch_size:,}\\n"\\
        f' "-____-"     Trainable parameters = {get_model_param_count(model, trainable_only=True):,} of {get_model_param_count(model):,} ({get_model_param_count(model, trainable_only=True)/get_model_param_count(model)*100:.2f}% trained)'
        logger.warning(debug_info)
        import gc
        for _ in range(3):
            gc.collect()
            if DEVICE_TYPE == "xpu":
                torch.xpu.empty_cache()
            else:
                torch.cuda.empty_cache()"""

        debug_info = debug_info.split("\n")
        debug_info = "\n".join([debug_info[0]] + [spaces + x[8:] for x in debug_info[1:]])
        inner_training_loop = inner_training_loop.replace(original_debug, debug_info)

        debug_info = """n_total_devices = total_train_batch_size // \\
            args.gradient_accumulation_steps // self._train_batch_size
        if n_total_devices > 1:
            logger.warning_once('Unsloth is running with multi GPUs - the effective batch size is multiplied by ' + str(n_total_devices))
        debug_info ="""
        debug_info = debug_info.split("\n")
        debug_info = "\n".join([debug_info[0]] + [spaces + x[8:] for x in debug_info[1:]])
        inner_training_loop = inner_training_loop.replace("debug_info =", debug_info, 1)

        front_spaces = re.match(r"[\t\s]{1,}", inner_training_loop).group(0)
        inner_training_loop = re.sub(
            r"^" + front_spaces, "", inner_training_loop, flags = re.MULTILINE
        )
        inner_training_loop = inner_training_loop.replace(
            "train_dataloader = tpu_spmd_dataloader(train_dataloader)",
            "raise RuntimeError('Unsloth: TPUs are not yet supported!')",
        )
        inner_training_loop = inner_training_loop.replace(
            "_inner_training_loop",
            "_fast_inner_training_loop",
            1,
        )
        inner_training_loop = inner_training_loop.replace(
            "is_torch_tpu_available()",
            "False",
        )
        # Wire the stray-forward compile-cache reset into the plain Trainer path: get_peft_model
        # arms the pre-train detector for every LoRA model, but only the TRL SFT/RL wrappers run
        # the reset. A grad-enabled probe before a bare transformers.Trainer.train() would
        # otherwise keep the poisoned Dynamo cache and leave the detector hook installed. Anchored
        # on the first body statement; a no-op (and harmless) if upstream drops that line.
        inner_training_loop = inner_training_loop.replace(
            "self.accelerator.free_memory()",
            "self.accelerator.free_memory()\n"
            "    try:\n"
            "        from unsloth.models._utils import _unsloth_reset_stray_compile_cache as _unsloth_reset_cc\n"
            "        _unsloth_reset_cc(self)\n"
            "    except Exception: pass",
            1,
        )
        exec(inner_training_loop, globals())
        Trainer._inner_training_loop = _fast_inner_training_loop

        # Save max_seq_length
        model.max_seq_length = max_seq_length
        m = model
        while hasattr(m, "model"):
            m.max_seq_length = max_seq_length
            m = m.model
        m.max_seq_length = max_seq_length
        # Save to modules as well
        for module in model.modules():
            module.max_seq_length = max_seq_length

        # We check the tokenizer first for errors
        if fix_tokenizer:
            tokenizer = check_tokenizer(
                model = model,
                tokenizer = tokenizer,
                model_name = model_name,
                model_max_length = max_position_embeddings,
                padding_side = "right",
                token = token,
                cache_dir = kwargs.get("cache_dir"),
            )
        patch_saving_functions(tokenizer)

        # Fix up config for transformers uploading PEFT
        # Not necessary anymore since we require transformers>=4.37!
        if False:
            name = model.config._name_or_path
            if name.startswith("unsloth/") and name.endswith("-bnb-4bit"):
                name = name[: len(name) - len("-bnb-4bit")]
                model.config.update({"_name_or_path": name})

        # Log Unsloth version for future fastpaths for inference
        model.config.update({"unsloth_version": __version__})

        # Add save modules
        patch_saving_functions(model)
        Trainer._inner_training_loop = _fast_inner_training_loop

        # Fix gradient accumulation. See issue #4982.
        apply_accepts_loss_kwargs_fix(model)
        patch_gradient_accumulation_fix(Trainer)

        # Save tokenizer for inference purposes
        tokenizer.padding_side = "left"  # Force inference
        internal_model = model
        while hasattr(internal_model, "model"):
            internal_model._saved_temp_tokenizer = tokenizer

            internal_model = internal_model.model
        internal_model._saved_temp_tokenizer = tokenizer
        # Prevent Transformers Trainer from auto-wrapping Unsloth LoRA models in DP.
        _mark_unsloth_disable_data_parallel(model)

        # For transformers > 4.47.1, we need to add rotary_emb to all attention layers
        if IS_ATTENTION_REFACTOR or hasattr(model.model, "rotary_emb"):
            rotary_emb = model.model.rotary_emb
            for layer in model.model.layers:
                layer.self_attn.rotary_emb = rotary_emb

        # Add for_inference and for_training
        model.for_training = functools.partial(FastLlamaModel.for_training, model)
        model.for_inference = functools.partial(FastLlamaModel.for_inference, model)
        m = model
        while hasattr(m, "model"):
            m.for_training = functools.partial(FastBaseModel.for_training, m)
            m.for_inference = functools.partial(FastBaseModel.for_inference, m)
            m = m.model

        # Patch generate
        is_classification = "Classification" in str(type(model))
        if not is_classification and model.generate.__name__ != "unsloth_fast_generate":
            model._old_generate = model.generate
            unsloth_fast_generate.__doc__ = model._old_generate.__doc__
            model.generate = types.MethodType(unsloth_fast_generate, model)
        # Zero weight[padding_idx] only for embeddings NOT tied to lm_head: when
        # tied, zeroing the row forces pad logit = 0, which beats the (negative)
        # logits of real tokens (e.g. Gemma) and makes the decoder emit <pad>.
        # Skip if eos_token == pad_token to avoid zeroing the EOS embedding.
        eos_token_id = getattr(tokenizer, "eos_token_id", None) if tokenizer is not None else None
        pad_token_id = getattr(tokenizer, "pad_token_id", None) if tokenizer is not None else None
        if tokenizer is not None and eos_token_id != pad_token_id:
            lm_head = getattr(model, "lm_head", None)
            lm_head_weight = getattr(lm_head, "weight", None) if lm_head is not None else None
            with torch.no_grad():
                for name, module in model.named_modules():
                    if type(module) is torch.nn.Embedding:
                        if (
                            getattr(module, "weight", None) is not None
                            and getattr(module, "padding_idx", None) is not None
                        ):
                            if module.padding_idx < module.weight.shape[0]:
                                # Skip if tied to lm_head
                                if (
                                    lm_head_weight is not None
                                    and module.weight.data_ptr() == lm_head_weight.data_ptr()
                                ):
                                    continue
                                module.weight[module.padding_idx] = 0
        return model, tokenizer

    @staticmethod
    def post_patch(
        model,
        tokenizer,
        correct_dtype = None,
    ):
        model, tokenizer = patch_model_and_tokenizer(
            model, tokenizer, downcast_rope = True, correct_dtype = correct_dtype
        )
        return model, tokenizer

    @staticmethod
    def get_peft_model(
        model,
        r = 16,
        target_modules = [
            "q_proj",
            "k_proj",
            "v_proj",
            "o_proj",
            "gate_proj",
            "up_proj",
            "down_proj",
        ],
        lora_alpha = 16,
        lora_dropout = 0.0,
        bias = "none",
        layers_to_transform = None,
        layers_pattern = None,
        finetune_last_n_layers = None,
        use_gradient_checkpointing = "unsloth",
        random_state = 3407,
        max_seq_length = 2048,  # not used anymore
        use_rslora = False,
        modules_to_save = None,
        init_lora_weights = True,
        loftq_config = {},
        temporary_location = "_unsloth_temporary_saved_buffers",
        qat_scheme = None,
        target_parameters = None,  # For MoE expert layers (nn.Parameter)
        ensure_weight_tying = False,
        **kwargs,
    ):
        if os.environ.get("UNSLOTH_USE_NEW_MODEL", "0") == "1":
            # Check for other PEFT args in kwargs
            for peft_arg, flag in (
                ("finetune_vision_layers", False),
                ("finetune_language_layers", True),
                ("finetune_attention_modules", True),
                ("finetune_mlp_modules", True),
                ("finetune_audio_layers", False),
            ):
                if peft_arg not in kwargs:
                    kwargs[peft_arg] = flag
            return FastBaseModel.get_peft_model(
                model = model,
                r = r,
                target_modules = target_modules,
                lora_alpha = lora_alpha,
                lora_dropout = lora_dropout,
                bias = bias,
                layers_to_transform = layers_to_transform,
                layers_pattern = layers_pattern,
                finetune_last_n_layers = finetune_last_n_layers,
                use_gradient_checkpointing = use_gradient_checkpointing,
                random_state = random_state,
                max_seq_length = max_seq_length,
                use_rslora = use_rslora,
                modules_to_save = modules_to_save,
                init_lora_weights = init_lora_weights,
                loftq_config = loftq_config,
                temporary_location = temporary_location,
                target_parameters = target_parameters,
                ensure_weight_tying = ensure_weight_tying,
                **kwargs,
            )
        if os.environ.get("UNSLOTH_ENABLE_FULL_FINETUNING", "0") == "1":
            print("Unsloth: Full finetuning is enabled, so .get_peft_model has no effect")
            # Full finetuning still compiles, so a stray pre-train forward can poison the
            # cache; install the detector here too (it is idempotent).
            _unsloth_install_pretrain_detector(model)
            return model
        transformers_set_seed(random_state)

        # Apply gradient checkpointing with smart heuristics
        max_seq = getattr(model, "max_seq_length", 512)
        dtype = model.get_input_embeddings().weight.dtype
        use_gradient_checkpointing = apply_unsloth_gradient_checkpointing(
            use_gradient_checkpointing, max_seq, dtype
        )

        if type(r) is not int:
            raise TypeError(f"Unsloth: Rank of {str(r)} must be an integer.")
        if r <= 0:
            raise TypeError(f"Unsloth: Rank of {str(r)} must be larger than 0.")

        if isinstance(model, PeftModelForCausalLM) or isinstance(
            model, PeftModelForSequenceClassification
        ):
            # Check if exactly the same and then pass through!
            assert hasattr(model, "peft_config")

            peft_config = model.peft_config["default"].to_dict()
            check_parameters = [
                "r",
                "lora_alpha",
                "lora_dropout",
                "bias",
                "layers_to_transform",
                "layers_pattern",
                "use_rslora",
                "init_lora_weights",
            ]
            check_all = True
            for param in check_parameters:
                check_all = check_all and (peft_config[param] == eval(param))

            # Check save_modules
            old_target_modules = list(peft_config["target_modules"])
            modules_to_save = peft_config["modules_to_save"]
            if modules_to_save is None:
                modules_to_save = {}
            modules_to_save = list(modules_to_save)
            old_target_modules += modules_to_save

            # Combine all
            new_target_modules = list(target_modules) + list(
                modules_to_save if modules_to_save is not None else []
            )
            # Per-expert Linear MoE experts (e.g. gpt-oss bnb-4bit) were auto-added to the
            # saved target_modules when the adapter was first created. Recompute them so a
            # repeat get_peft_model call with the same args stays idempotent instead of
            # tripping the mismatch below. No-op for non per-expert-Linear models.
            new_target_modules += get_moe_target_modules(model, target_modules)

            # Now check!
            new_target_modules = set(new_target_modules)
            check_all = check_all and (len(set(old_target_modules) ^ new_target_modules) == 0)

            check_all = check_all and (
                (loftq_config == {} or loftq_config is None)
                and (peft_config["loftq_config"] == {} or peft_config["loftq_config"] is None)
            )

            if check_all:
                # Simply pass through!
                logger.warning("Unsloth: Already have LoRA adapters! We shall skip this step.")

                # Offload!
                # [TODO] First offload lm_head and embed_tokens to CPU (should be disk!!)
                if "embed_tokens" in new_target_modules:
                    print("Unsloth: Training embed_tokens in mixed precision to save VRAM")

                    _offload_frozen_module_for_training(
                        model.get_input_embeddings(), DEVICE_TYPE_TORCH
                    )

                if "lm_head" in new_target_modules:
                    print("Unsloth: Training lm_head in mixed precision to save VRAM")

                    _offload_frozen_module_for_training(
                        model.get_output_embeddings(), DEVICE_TYPE_TORCH
                    )

                # Pre-wrapped PEFT model passes through here; still arm the detector so an RL
                # trainer can reset a compile cache poisoned by a pre-train forward.
                _unsloth_install_pretrain_detector(model)
                # This branch returns before patch_peft_model, so record here too;
                # apply_unsloth_gradient_checkpointing above already re-patched global state to match (#4735).
                model._unsloth_gradient_checkpointing = use_gradient_checkpointing
                model = _exclude_rope_inv_freq_from_ddp(model)
                return model
            else:
                raise TypeError(
                    "Unsloth: Your model already has LoRA adapters. Your new parameters are different."
                )

        if loftq_config is None:
            loftq_config = {}

        signature = str(inspect.signature(LoraConfig))
        SUPPORTS_LOFTQ = "loftq_config" in signature
        SUPPORTS_RSLORA = "use_rslora" in signature

        if lora_dropout != 0:
            logger.warning_once(
                f"Unsloth: Dropout = 0 is supported for fast patching. You are using dropout = {lora_dropout}.\n"
                f"Unsloth will patch all other layers, except LoRA matrices, causing a performance hit."
            )

        if bias != "none":
            logger.warning_once(
                f"Unsloth: bias = `none` is supported for fast patching. You are using bias = {bias}.\n"
                f"Unsloth will patch all other layers, except LoRA matrices, causing a performance hit."
            )

        if not (
            type(init_lora_weights) is bool
            or init_lora_weights == "gaussian"
            or init_lora_weights == "loftq"
            or init_lora_weights == "corda"
        ):
            raise ValueError(
                'Unsloth: `init_lora_weights` must be either [True, False, "gaussian", "loftq", "corda"].'
            )

        if init_lora_weights == "loftq":
            if not SUPPORTS_LOFTQ:
                import peft
                raise RuntimeError(
                    f"Unsloth: Your PEFT version of {peft.__version__} does not support LoftQ init.\n"
                    "Please install PEFT 0.7.2 or higher.\n"
                    "You can also install from source: `pip install git+https://github.com/huggingface/peft.git"
                )

            if loftq_config == {}:
                from peft import LoftQConfig
                logger.warning_once(
                    "Unsloth: init_lora_weights = `loftq` is set, but `loftq_config` is None.\n"
                    "We shall use `loftq_config = LoftQConfig(loftq_bits = 4, loftq_iter = 1)`."
                )
                loftq_config = LoftQConfig(loftq_bits = 4, loftq_iter = 1)

            if hasattr(model.config, "quantization_config"):
                raise ValueError(
                    "Unsloth: You are using `loftq` init, yet `load_in_4bit = True` was set.\n"
                    "Reload your model without any quantization by setting `load_in_4bit = False`."
                )

        assert type(use_rslora) is bool
        if use_rslora:
            if not SUPPORTS_RSLORA:
                # We manually check for PEFT
                import peft
                raise RuntimeError(
                    f"Unsloth: Your PEFT version of {peft.__version__} does not support `use_rslora`.\n"
                    "Please install PEFT 0.7.2 or higher.\n"
                    "You can also install from source: `pip install git+https://github.com/huggingface/peft.git"
                )

        accepted_modules = frozenset(
            (
                "lm_head",
                "q_proj",
                "k_proj",
                "v_proj",
                "o_proj",
                "gate_proj",
                "up_proj",
                "down_proj",
            ),
        )
        model.config.update({"unsloth_version": __version__})

        if type(modules_to_save) is tuple:
            modules_to_save = list(modules_to_save)

        train_lm_head = False
        train_embed_tokens = False
        final_modules = []
        for module in target_modules:
            if module == "embed_tokens":
                # logger.warning_once(
                #     "Unsloth: `embed_tokens` should be placed in `modules_to_save` and not `target_modules`. "\
                #     "Luckily, we shall do it for you!"
                # )
                train_embed_tokens = True
                if modules_to_save is None:
                    modules_to_save = ["embed_tokens"]
                else:
                    modules_to_save.append("embed_tokens")

            else:
                try:
                    assert module in accepted_modules
                    final_modules.append(module)
                except AssertionError as e:
                    final_modules.append(module)
                    print(
                        "Unsloth: You added custom modules, but Unsloth hasn't optimized for this.\n"
                        "Beware - your finetuning might be noticeably slower!"
                    )
                pass

        # Check if we added new tokens!
        if hasattr(model, "_need_to_train_embeddings"):
            # Check if embed_tokens/lm_head are already being trained
            # (either as LoRA targets in final_modules or via modules_to_save)
            _embed_already_trained = train_embed_tokens or "embed_tokens" in final_modules
            _lm_head_already_trained = train_lm_head or "lm_head" in final_modules
            if not _lm_head_already_trained or not _embed_already_trained:
                print(
                    "Unsloth: You added new tokens but did not specify if you wanted to "
                    "train the lm_head and embed_tokens.\nWe must turn it on for you."
                )

                # Only add to modules_to_save if not already a LoRA target
                if not _embed_already_trained:
                    train_embed_tokens = True
                    if modules_to_save is None:
                        modules_to_save = ["embed_tokens"]
                    elif "embed_tokens" not in modules_to_save:
                        modules_to_save.append("embed_tokens")

                if not _lm_head_already_trained:
                    train_lm_head = True
                    if modules_to_save is None:
                        modules_to_save = ["lm_head"]
                    elif "lm_head" not in modules_to_save:
                        modules_to_save.append("lm_head")

        # Check for Llama-3
        # if hasattr(model._saved_temp_tokenizer, "_using_llama3_template"):
        #     if not train_embed_tokens and not train_lm_head:
        #         raise RuntimeError("")

        # First fix untrained tokens
        # Wrong - can cause reserved tokens to pop out!!
        # if train_embed_tokens or train_lm_head:
        #     fix_untrained_tokens(model, eps = 1e-16)
        # pass

        # Check modules_to_save
        if modules_to_save is not None:
            for module in modules_to_save:
                if module == "lm_head":
                    train_lm_head = True
                elif module == "embed_tokens":
                    train_embed_tokens = True
                else:
                    raise TypeError(
                        f"Unsloth: Module = {module} is not allowed. Only 'lm_head' and 'embed_tokens' is allowed."
                    )
        if isinstance(modules_to_save, (tuple, list)):
            modules_to_save = list(set(modules_to_save))

        vllm_engine = None
        if hasattr(model, "vllm_engine"):
            # Fast inference!
            vllm_engine = model.vllm_engine
            vllm_fast_generate = model.fast_generate
            vllm_fast_generate_batches = model.fast_generate_batches

            if modules_to_save is not None:
                raise NotImplementedError(
                    "Unsloth: Currently fast inference does not work with training embeddings or lm_head."
                )

            if bias != "none":
                raise NotImplementedError(
                    "Unsloth: Currently fast inference does not work with using biases for LoRA."
                )

        # Does not get lora yet, so get name from model, not base model
        is_classification = "Classification" in str(type(model))

        # Auto-detect MoE models and populate target_parameters for expert layers
        if target_parameters is None:
            target_parameters = get_moe_target_parameters(model, target_modules)

        # Per-expert Linear expert layouts (e.g. gpt-oss bnb-4bit) are Linear modules,
        # not fused Parameters, so target them via target_modules. No-op otherwise.
        _moe_module_targets = get_moe_target_modules(model, target_modules)
        if _moe_module_targets:
            _added = [t for t in _moe_module_targets if t not in final_modules]
            final_modules.extend(_added)
            if _added:
                print(
                    f"Unsloth: Detected MoE model with per-expert Linear experts. "
                    f"Enabling LoRA on {len(_added)} expert projection modules."
                )
                warn_if_zoo_cannot_merge_moe_experts()

        if finetune_last_n_layers is not None and layers_to_transform is None:
            from .vision import _get_total_transformer_layers
            _total_layers = _get_total_transformer_layers(model)
            if _total_layers is not None and _total_layers > 0:
                _n = max(1, min(int(finetune_last_n_layers), _total_layers))
                layers_to_transform = list(range(_total_layers - _n, _total_layers))

        arguments = dict(
            r = r,
            lora_alpha = lora_alpha,
            target_modules = final_modules,
            lora_dropout = lora_dropout,
            bias = bias,
            task_type = TaskType.CAUSAL_LM if not is_classification else TaskType.SEQ_CLS,
            layers_to_transform = layers_to_transform,
            init_lora_weights = init_lora_weights,
            loftq_config = loftq_config,
            use_rslora = use_rslora,
            modules_to_save = modules_to_save,
            target_parameters = target_parameters,
            ensure_weight_tying = ensure_weight_tying,
            **kwargs,
        )
        if not SUPPORTS_LOFTQ:
            del arguments["loftq_config"]
        if not SUPPORTS_RSLORA:
            del arguments["use_rslora"]

        _saved_temp_tokenizer = model._saved_temp_tokenizer

        lora_config = LoraConfig(**arguments)
        # First offload lm_head and embed_tokens to disk
        input_embeddings_device = model.get_input_embeddings().weight.device
        if is_classification:
            output_embeddings_device = model.score.weight.device
        else:
            output_embeddings_device = model.get_output_embeddings().weight.device

        if use_gradient_checkpointing == "unsloth":
            if train_embed_tokens:
                print("Unsloth: Offloading input_embeddings to disk to save VRAM")
                offload_input_embeddings(model, temporary_location)

            # Remove old items to save VRAM
            for _ in range(3):
                gc.collect()
                clean_gpu_cache()

            if train_lm_head:
                print("Unsloth: Offloading output_embeddings to disk to save VRAM")
                offload_output_embeddings(model, temporary_location)

            # Remove old items to save VRAM
            for _ in range(3):
                gc.collect()
                clean_gpu_cache()

        model = _get_peft_model(model, lora_config)
        # Fix LoraConfig.auto_mapping is None
        fix_lora_auto_mapping(model)

        # Apply QAT + LoRA if specified
        if qat_scheme is not None:
            print("Unsloth: Applying QAT to mitigate quantization degradation")
            model = FastLlamaModel._prepare_for_qat(model, qat_scheme)

        model._saved_temp_tokenizer = _saved_temp_tokenizer

        model = FastLlamaModel.patch_peft_model(model, use_gradient_checkpointing)

        if ensure_weight_tying:
            try:
                input_embeddings = model.get_input_embeddings()
                output_embeddings = model.get_output_embeddings()

                if input_embeddings is not None and output_embeddings is not None:

                    def _retie_parameter(target_module, source_module):
                        if not hasattr(source_module, "weight"):
                            return
                        weight = source_module.weight
                        # Remove existing registration to avoid "attribute already exists"
                        if "weight" in getattr(target_module, "_parameters", {}):
                            target_module._parameters.pop("weight")
                        if hasattr(target_module, "weight"):
                            try:
                                delattr(target_module, "weight")
                            except Exception as exc:
                                logger.warning_once(
                                    f"Unsloth: Could not delete existing weight attr during retie on "
                                    f"{type(target_module).__name__}: {exc}"
                                )
                        target_module.register_parameter("weight", weight)

                    # Tie trainable copies created by ModulesToSaveWrapper first (these are used in forward)
                    if hasattr(input_embeddings, "modules_to_save") and hasattr(
                        output_embeddings, "modules_to_save"
                    ):
                        if hasattr(input_embeddings.modules_to_save, "default") and hasattr(
                            output_embeddings.modules_to_save, "default"
                        ):
                            _retie_parameter(
                                output_embeddings.modules_to_save.default,
                                input_embeddings.modules_to_save.default,
                            )

                    # Tie original_module references as well if present
                    if hasattr(input_embeddings, "original_module") and hasattr(
                        output_embeddings, "original_module"
                    ):
                        _retie_parameter(
                            output_embeddings.original_module,
                            input_embeddings.original_module,
                        )
            except Exception as e:
                logger.warning_once(
                    f"Unsloth: Failed to ensure weight tying between embeddings and lm_head: {e}"
                )

        if train_embed_tokens:
            print("Unsloth: Training embed_tokens in mixed precision to save VRAM")
            assert hasattr(model.get_input_embeddings(), "modules_to_save")

            _offload_frozen_module_for_training(
                model.get_input_embeddings(), DEVICE_TYPE_TORCH, offload_device = None
            )

        if train_lm_head:
            print("Unsloth: Training lm_head in mixed precision to save VRAM")
            assert hasattr(model.get_output_embeddings(), "modules_to_save")

            _offload_frozen_module_for_training(
                model.get_output_embeddings(), DEVICE_TYPE_TORCH, offload_device = None
            )

        # Patch tokenizer to pad to the right
        internal_model = model
        while hasattr(internal_model, "model"):
            if hasattr(internal_model, "_saved_temp_tokenizer"):
                internal_model._saved_temp_tokenizer.padding_side = "right"
            internal_model = internal_model.model
        if hasattr(internal_model, "_saved_temp_tokenizer"):
            internal_model._saved_temp_tokenizer.padding_side = "right"
        # Prevent Transformers Trainer from auto-wrapping Unsloth LoRA models in DP.
        _mark_unsloth_disable_data_parallel(model)

        # Clear deleted GPU items
        for _ in range(3):
            gc.collect()
            clean_gpu_cache()

        patch_peft_fast_inference(model)

        # Add for_inference and for_training
        model.for_training = functools.partial(FastLlamaModel.for_training, model)
        model.for_inference = functools.partial(FastLlamaModel.for_inference, model)
        m = model
        while hasattr(m, "model"):
            m.for_training = functools.partial(FastBaseModel.for_training, m)
            m.for_inference = functools.partial(FastBaseModel.for_inference, m)
            m = m.model
        # Detect a stray pre-train forward so train() can drop the torch.compile
        # graph cache it would otherwise poison (see prepare_for_training_mode).
        _unsloth_install_pretrain_detector(model)
        model = _exclude_rope_inv_freq_from_ddp(model)
        return model

    @staticmethod
    def patch_peft_model(model, use_gradient_checkpointing = "unsloth"):
        # Persist the effective GC mode so the trainer restores it verbatim: for_inference()
        # clears the module flags every GRPO step, and a plain TrainingArguments defaults it to
        # False, which would otherwise silently disable it at train time (#4735). Recorded here,
        # not in get_peft_model, so adapters loaded via loader.py's from_pretrained path are covered.
        model._unsloth_gradient_checkpointing = use_gradient_checkpointing
        if os.environ.get("UNSLOTH_USE_NEW_MODEL", "0") == "1":
            return FastBaseModel.patch_peft_model(
                model = model,
                use_gradient_checkpointing = use_gradient_checkpointing,
            )
        if not isinstance(model, PeftModelForCausalLM) and not isinstance(
            model, PeftModelForSequenceClassification
        ):
            raise TypeError("Unsloth: Your model needs to call `.get_peft_model` first!")

        # Get activation function
        model_type = model.config.model_type

        if model_type == "llama":
            apply_lora_mlp = apply_lora_mlp_swiglu
        elif model_type == "mistral":
            apply_lora_mlp = apply_lora_mlp_swiglu
        elif model_type == "qwen2":
            apply_lora_mlp = apply_lora_mlp_swiglu
        elif model_type == "gemma":
            apply_lora_mlp = apply_lora_mlp_geglu_approx
        elif model_type == "gemma2":
            apply_lora_mlp = apply_lora_mlp_geglu_approx
        elif model_type == "cohere":
            apply_lora_mlp = apply_lora_mlp_swiglu
        elif model_type == "granite":
            apply_lora_mlp = apply_lora_mlp_swiglu
        elif model_type == "qwen3":
            apply_lora_mlp = apply_lora_mlp_swiglu
        elif model_type == "falcon_h1":
            apply_lora_mlp = apply_lora_mlp_swiglu
        elif model_type == "qwen3moe":
            apply_lora_mlp = apply_lora_mlp_swiglu
        else:
            raise NotImplementedError(f"Unsloth: {model_type} is not yet implemented!")

        model = prepare_model_for_kbit_training(
            model,
            use_gradient_checkpointing = use_gradient_checkpointing,
            use_reentrant = True,
        )

        # Fix up config for transformers uploading PEFT
        for active_adapter in model.peft_config.keys():
            # Not necessary since we requires transformers >= 4.37
            if False:
                name = model.peft_config[active_adapter].base_model_name_or_path
                if name.startswith("unsloth/") and name.endswith("-bnb-4bit"):
                    name = name[: len(name) - len("-bnb-4bit")]
                    model.peft_config[active_adapter].base_model_name_or_path = name
                pass
            # Add revision to enable future fast inference paths
            # [TODO] Bugs out!see https://github.com/unslothai/unsloth/issues/492
            # model.peft_config[active_adapter].revision = f"unsloth"

        from transformers.trainer import Trainer

        if Trainer._inner_training_loop.__name__ != "_fast_inner_training_loop":
            raise RuntimeError("Unsloth: Unsuccessfully patched Trainer! Please file a bug report!")

        # Fix loftq issues
        # loftq_config must not = None, but rather {}
        all_configs = model.peft_config
        for key, current_config in all_configs.items():
            if hasattr(current_config, "loftq_config") and current_config.loftq_config is None:
                new_args = current_config.__dict__
                new_args["loftq_config"] = {}
                current_config = current_config.__class__(**new_args)
                all_configs[key] = current_config

        # Do patching
        n_mlp = 0
        n_qkv = 0
        n_o = 0

        active_adapter = (
            model.active_adapters[0] if hasattr(model, "active_adapters") else model.active_adapter
        )

        # Get dropout and bias
        lora_dropout = model.peft_config[active_adapter].lora_dropout
        bias = model.peft_config[active_adapter].bias

        # We also do not inplace edit QKV for Cohere!
        _apply_lora_mlp = (
            functools.partial(apply_lora_mlp, inplace = False)
            if model_type == "cohere"
            else apply_lora_mlp
        )

        if lora_dropout == 0 and bias == "none":
            for idx, layer in enumerate(model.model.model.layers):
                if model_type != "falcon_h1":
                    # LoRAMLP.apply doesn't have functionality for gate and down multipliers yet.
                    # Don't patch falcon h1 for the time being.

                    # MLP patching
                    mlp_module = layer.mlp
                    gate_proj = mlp_module.gate_proj
                    up_proj = mlp_module.up_proj
                    down_proj = mlp_module.down_proj

                    if (
                        hasattr(gate_proj, "lora_A")
                        and hasattr(up_proj, "lora_A")
                        and hasattr(down_proj, "lora_A")
                        and (getattr(gate_proj, "base_layer", gate_proj).bias is None)
                        and (getattr(up_proj, "base_layer", up_proj).bias is None)
                        and (getattr(down_proj, "base_layer", down_proj).bias is None)
                        and (len(getattr(gate_proj, "lora_magnitude_vector", []) or []) == 0)
                        and (len(getattr(up_proj, "lora_magnitude_vector", []) or []) == 0)
                        and (len(getattr(down_proj, "lora_magnitude_vector", []) or []) == 0)
                    ):
                        # https://stackoverflow.com/questions/50599045/python-replacing-a-function-within-a-class-of-a-module
                        if hasattr(mlp_module, "_unsloth_forward"):
                            # then we've patched the mlp to use TiledMLP
                            mlp_module._unsloth_forward = types.MethodType(
                                _apply_lora_mlp, mlp_module
                            )
                        else:
                            mlp_module.forward = types.MethodType(_apply_lora_mlp, mlp_module)
                        n_mlp += 1
                    else:
                        logger.warning_once(
                            "Not an error, but Unsloth cannot patch MLP layers with our manual autograd engine since either LoRA adapters\n"
                            "are not enabled or a bias term (like in Qwen) is used."
                        )

                # QKV attention patching
                q_proj = layer.self_attn.q_proj
                k_proj = layer.self_attn.k_proj
                v_proj = layer.self_attn.v_proj
                if (
                    hasattr(q_proj, "lora_A")
                    and hasattr(k_proj, "lora_A")
                    and hasattr(v_proj, "lora_A")
                    and (getattr(q_proj, "base_layer", q_proj).bias is None)
                    and (getattr(k_proj, "base_layer", k_proj).bias is None)
                    and (getattr(v_proj, "base_layer", v_proj).bias is None)
                    and (len(getattr(q_proj, "lora_magnitude_vector", []) or []) == 0)
                    and (len(getattr(k_proj, "lora_magnitude_vector", []) or []) == 0)
                    and (len(getattr(v_proj, "lora_magnitude_vector", []) or []) == 0)
                ):
                    layer.self_attn.apply_qkv = apply_lora_qkv
                    n_qkv += 1
                else:
                    if model_type == "qwen2":
                        n_qkv += 1
                    else:
                        logger.warning_once(
                            "Not an error, but Unsloth cannot patch Attention layers with our manual autograd engine since either LoRA adapters\n"
                            "are not enabled or a bias term (like in Qwen) is used."
                        )

                # O attention patching
                o_proj = layer.self_attn.o_proj
                if (
                    hasattr(o_proj, "lora_A")
                    and (getattr(o_proj, "base_layer", o_proj).bias is None)
                    and (len(getattr(o_proj, "lora_magnitude_vector", []) or []) == 0)
                ):
                    layer.self_attn.apply_o = apply_lora_o
                    n_o += 1
                else:
                    logger.warning_once(
                        "Not an error, but Unsloth cannot patch O projection layer with our manual autograd engine since either LoRA adapters\n"
                        "are not enabled or a bias term (like in Qwen) is used."
                    )

        logger.warning_once(
            f"Unsloth {__version__} patched {len(model.model.model.layers)} layers with "
            f"{n_qkv} QKV layers, {n_o} O layers and {n_mlp} MLP layers.",
        )
        patch_saving_functions(model)

        # Patch cross entropy loss labels
        # Fixes https://github.com/unslothai/unsloth/issues/10
        max_seq_length = model.max_seq_length
        # extra_ignored_labels = torch.full((max_seq_length, 1), -100, device = "cuda:0")
        # model.model.extra_ignored_labels = extra_ignored_labels
        internal_model = model
        while hasattr(internal_model, "model"):
            internal_model.max_seq_length = max_seq_length
            internal_model = internal_model.model
        internal_model.max_seq_length = max_seq_length
        # Save to modules as well
        for module in model.modules():
            module.max_seq_length = max_seq_length

        # Patch tokenizer to pad to the right
        internal_model = model
        while hasattr(internal_model, "model"):
            if hasattr(internal_model, "_saved_temp_tokenizer"):
                internal_model._saved_temp_tokenizer.padding_side = "right"
            internal_model = internal_model.model
        if hasattr(internal_model, "_saved_temp_tokenizer"):
            internal_model._saved_temp_tokenizer.padding_side = "right"

        # Clear deleted GPU items
        for _ in range(3):
            gc.collect()
            clean_gpu_cache()

        patch_peft_fast_inference(model)

        # Add for_inference and for_training
        model.for_training = functools.partial(FastLlamaModel.for_training, model)
        model.for_inference = functools.partial(FastLlamaModel.for_inference, model)
        m = model
        while hasattr(m, "model"):
            m.for_training = functools.partial(FastBaseModel.for_training, m)
            m.for_inference = functools.partial(FastBaseModel.for_inference, m)
            m = m.model
        # Detect a stray pre-train forward so train() can drop the torch.compile
        # graph cache it would otherwise poison (see prepare_for_training_mode).
        _unsloth_install_pretrain_detector(model)
        return model

    @staticmethod
    def for_inference(model):
        if not hasattr(model, "parameters"):
            raise TypeError(
                "Unsloth: I think you're passing a tokenizer, not the model to for_inference!"
            )

        def _for_inference(m):
            if hasattr(m, "gradient_checkpointing"):
                m.gradient_checkpointing = False
            if hasattr(m, "training"):
                m.training = False
            # Pad tokenizer to the left
            if hasattr(m, "_saved_temp_tokenizer"):
                m._saved_temp_tokenizer.padding_side = "left"
            # Set a flag for generation!
            m._flag_for_generation = True

        m = model
        while hasattr(m, "model"):
            _for_inference(m)
            m = m.model
        _for_inference(m)
        model.eval()  # to turn off training on modules deeper in

        # Since transformers 4.53, must turn off explicitly
        for module in model.modules():
            if hasattr(module, "gradient_checkpointing"):
                module.gradient_checkpointing = False

        # Also disable training for embeddings for NEFTune
        if hasattr(model, "get_input_embeddings"):
            embeddings = model.get_input_embeddings()
            if hasattr(embeddings, "training"):
                embeddings.training = False
        if hasattr(model, "get_output_embeddings"):
            embeddings = model.get_output_embeddings()
            if hasattr(embeddings, "training"):
                embeddings.training = False

        # Restore use_cache values that prepare_model_for_training disabled
        # for gradient checkpointing (older unsloth_zoo has no restore helper)
        try:
            from unsloth_zoo.training_utils import restore_use_cache
            restore_use_cache(model)
        except ImportError:
            pass
        return model

    @staticmethod
    def for_training(model, use_gradient_checkpointing = True):
        if not hasattr(model, "parameters"):
            raise TypeError(
                "Unsloth: I think you're passing a tokenizer, not the model to for_training!"
            )

        # Delete all fast inference loras
        for param in model.parameters():
            if hasattr(param, "_fast_lora"):
                del param._fast_lora

        def _for_training(m):
            if hasattr(m, "gradient_checkpointing"):
                m.gradient_checkpointing = use_gradient_checkpointing
            if hasattr(m, "training"):
                m.training = True
            # Pad tokenizer to the left
            if hasattr(m, "_saved_temp_tokenizer"):
                m._saved_temp_tokenizer.padding_side = "right"
            # Set a flag for generation!
            if hasattr(m, "_flag_for_generation"):
                del m._flag_for_generation

        m = model
        while hasattr(m, "model"):
            _for_training(m)
            m = m.model
        _for_training(m)
        model.train()  # to turn on training on modules deeper in

        # Since transformers 4.53, must turn on explicitly
        for module in model.modules():
            if hasattr(module, "gradient_checkpointing"):
                module.gradient_checkpointing = use_gradient_checkpointing

        # Also re-enable training for embeddings for NEFTune
        if hasattr(model, "get_input_embeddings"):
            embeddings = model.get_input_embeddings()
            if hasattr(embeddings, "training"):
                embeddings.training = True
        if hasattr(model, "get_output_embeddings"):
            embeddings = model.get_output_embeddings()
            if hasattr(embeddings, "training"):
                embeddings.training = True

        # Re-disable use_cache if prepare_model_for_training had disabled it
        # and for_inference restored it (record only exists after a disable)
        if (
            use_gradient_checkpointing
            and getattr(model, "_unsloth_use_cache_originals", None) is not None
        ):
            try:
                from unsloth_zoo.training_utils import disable_use_cache
                disable_use_cache(model)
            except ImportError:
                pass
        return model


from .rl import PatchFastRL

# Auto-enable grouped-GEMM MoE (tf<5 ModuleList experts) on built / PEFT'd models. Wrap the
# loader leaves before PatchFastRL so downstream patchers see the wrapped versions. Guarded.
try:
    from unsloth_zoo.temporary_patches.moe_grouped_modulelist import wrap_loader_for_grouped_moe
    FastLlamaModel.from_pretrained = staticmethod(
        wrap_loader_for_grouped_moe(FastLlamaModel.from_pretrained)
    )
    FastLlamaModel.get_peft_model = staticmethod(
        wrap_loader_for_grouped_moe(FastLlamaModel.get_peft_model)
    )
except Exception:
    pass

PatchFastRL(FastLanguageModel = FastLlamaModel)