modeling_motif.py

import math
from typing import List, Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss

import transformers
if int(transformers.__version__.split(".")[0]) >= 5:
    raise ImportError(
        f"This module requires transformers < 5.0.0, but found {transformers.__version__}. "
        "Please downgrade: pip install 'transformers<5'"
    )

from transformers.activations import ACT2CLS as _ACT2CLS
from transformers.activations import ClassInstantier
from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
from transformers.generation import GenerationMixin
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_flash_attention_utils import _flash_attention_forward
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    logging,
    replace_return_docstrings,
)

from .configuration_motif import MotifConfig
import kernels
try:
    activation = kernels.get_kernel("Motif-Technologies/activation")
except:
    activation = None
logger = logging.get_logger(__name__)

if is_flash_attn_2_available():
    from transformers.modeling_flash_attention_utils import _flash_attention_forward

import einops

try:
    kernelRMSNorm = activation.layers.RMSNorm
    PolyNormKernel = activation.layers.PolyNorm
    logger.warning_once("Using kernel ops")
except AttributeError:
    kernelRMSNorm = None
    PolyNormKernel = None
    logger.warning_once("Failed to import kernel ops")

_CONFIG_FOR_DOC = "MotifConfig"


class PolyNormTorch(torch.nn.Module):
    """
    A trainable activation function introduced in https://arxiv.org/html/2411.03884v1.
    The code is copied from https://github.com/BryceZhuo/PolyCom?tab=readme-ov-file/README.md,
    with the change `* torch.rsqrt` => `/ torch.sqrt`.
    """

    def __init__(self, eps=1e-6):
        super(PolyNorm, self).__init__()
        self.weight = torch.nn.Parameter(torch.ones(3) / 3)
        self.bias = torch.nn.Parameter(torch.zeros(1))
        self.eps = eps

    def _norm(self, x):
        return x / torch.sqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward(self, x):
        return (
            self.weight[0] * self._norm(x**3)
            + self.weight[1] * self._norm(x**2)
            + self.weight[2] * self._norm(x)
            + self.bias
        )


PolyNorm = PolyNormKernel if PolyNormKernel is not None else PolyNormTorch
CUSTOM_ACT2CLS = {"poly_norm": PolyNorm}
ACT2CLS = {**_ACT2CLS, **CUSTOM_ACT2CLS}
ACT2FN = ClassInstantier(ACT2CLS)


class MotifRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        MotifRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

    def extra_repr(self):
        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"


class MotifRotaryEmbedding(nn.Module):
    def __init__(
        self,
        dim=None,
        max_position_embeddings=2048,
        base=10000,
        device=None,
        scaling_factor=1.0,
        rope_type="default",
        config: Optional[MotifConfig] = None,
    ):
        super().__init__()
        # TODO (joao): remove the `if` below, only used for BC
        self.rope_kwargs = {}
        if config is None:
            logger.warning_once(
                "`MotifRotaryEmbedding` can now be fully parameterized by passing the model config through the "
                "`config` argument. All other arguments will be removed in v4.46"
            )
            self.rope_kwargs = {
                "rope_type": rope_type,
                "factor": scaling_factor,
                "dim": dim,
                "base": base,
                "max_position_embeddings": max_position_embeddings,
            }
            self.rope_type = rope_type
            self.max_seq_len_cached = max_position_embeddings
            self.original_max_seq_len = max_position_embeddings
        else:
            # BC: "rope_type" was originally "type"
            if config.rope_scaling is not None:
                self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
            else:
                self.rope_type = "default"
            self.max_seq_len_cached = config.max_position_embeddings
            self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]

        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, **self.rope_kwargs)

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

    def _dynamic_frequency_update(self, position_ids, device):
        """
        dynamic RoPE layers should recompute `inv_freq` in the following situations:
        1 - growing beyond the cached sequence length (allow scaling)
        2 - the current sequence length is in the original scale (avoid losing precision with small sequences)
        """
        seq_len = torch.max(position_ids) + 1
        if seq_len > self.max_seq_len_cached:  # growth
            inv_freq, self.attention_scaling = self.rope_init_fn(
                self.config, device, seq_len=seq_len, **self.rope_kwargs
            )
            self.register_buffer("inv_freq", inv_freq, persistent=False)  # TODO joao: may break with compilation
            self.max_seq_len_cached = seq_len

        if seq_len < self.original_max_seq_len and self.max_seq_len_cached > self.original_max_seq_len:  # reset
            self.register_buffer("inv_freq", self.original_inv_freq, persistent=False)
            self.max_seq_len_cached = self.original_max_seq_len

    @torch.no_grad()
    def forward(self, x, position_ids):
        if "dynamic" in self.rope_type:
            self._dynamic_frequency_update(position_ids, device=x.device)

        # Core RoPE block
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
        position_ids_expanded = position_ids[:, None, :].float()
        # Force float32 (see https://github.com/huggingface/transformers/pull/29285)
        device_type = x.device.type
        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
        with torch.autocast(device_type=device_type, enabled=False):
            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
            emb = torch.cat((freqs, freqs), dim=-1)
            cos = emb.cos()
            sin = emb.sin()

        # Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
        cos = cos * self.attention_scaling
        sin = sin * self.attention_scaling

        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


def rotate_half(x):
    """
    Rotates half of the dimensions of the input tensor using torch.roll and in-place negation.

    Args:
    x (torch.Tensor): The input tensor.

    Returns:
    torch.Tensor: A tensor where the latter half of the dimensions are negated
                  and moved before the first half.
    """
    half_size = x.shape[-1] // 2
    rotated_tensor = torch.roll(x, shifts=-half_size, dims=-1)
    rotated_tensor[..., :half_size] *= -1

    return rotated_tensor


def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1, fused_rope=False):
    """
    Applies rotary position embeddings to the input tensors.
    Args:
        q (torch.Tensor): Query tensor of shape (B, NH, S, D_KV).
        k (torch.Tensor): Key tensor of shape (B, NH, S, D_KV).
        cos (torch.Tensor): Cosine values for rotary embedding.
        sin (torch.Tensor): Sine values for rotary embedding.
        unsqueeze_dim (int, optional): Dimension along which `cos` and `sin` are unsqueezed. 
            Defaults to 1.
    Returns:
        Tuple[torch.Tensor, torch.Tensor]: Returns transformed query and key tensors after applying rotary embeddings.
    """
    '''
    # (B, NH, S, D_KV) -> (B, S, NH, D_KV)  
    cos = cos.unsqueeze(unsqueeze_dim)
    sin = sin.unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    '''
    if fused_rope:
        raise NotImplementedError("Fused rotary embedding not yet supported.")

    device = q.device
    return map(
            lambda x: (x * cos.unsqueeze(unsqueeze_dim).to(device)) +
            (rotate_half(x) * sin.unsqueeze(unsqueeze_dim).to(device)), (q, k))


class MotifMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size

        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
        self.act_fn = ACT2FN[config.hidden_act]


    def forward(self, hidden_state):
        hidden_state = self.act_fn(self.gate_proj(hidden_state).float()).bfloat16() * self.up_proj(hidden_state)
        return self.down_proj(hidden_state)


def repeat_kv(hidden_states: torch.Tensor, dim: int, n_rep: int) -> torch.Tensor:
    return torch.repeat_interleave(hidden_states, dim=dim, repeats=n_rep)


class MotifAttention(nn.Module):
    """
    Grouped Differential Attention module.

    Implements the Grouped Differential Attention from
    "Grouped Differential Attention" (https://arxiv.org/pdf/2510.06949).

    Overview
        Standard transformers often over-allocate attention to irrelevant context.
        DiffAttention addresses this by computing attention as the difference between
        two separate softmax attention maps, effectively canceling noise and promoting
        sparse, structured attention patterns.

    Reference Implementation
        https://github.com/microsoft/unilm/tree/master/Diff-Transformer

    Args
        The differential attention mechanism computes attention as the difference of two softmax attention scores, weighted by a learnable scalar λ.
        λ is re-parameterized as λ = exp(λ_q1 · λ_k1) − exp(λ_q2 · λ_k2) + λ_init.
        - lambda_q1, lambda_q2 (nn.Parameter): Learnable vectors used to compute the first and second components of λ for query transformations.
        - lambda_k1, lambda_k2 (nn.Parameter): Learnable vectors used to compute the first and second components of λ for key transformations.
        - lambda_init (float): A constant used for initializing λ, typically set as λ_init = 0.8 − 0.6 × exp(−0.3 × (layer_index − 1)).

    """

    def __init__(self, config: MotifConfig, layer_idx: Optional[int] = None):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        if layer_idx is None:
            logger.warning_once(
                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )

        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads if config.head_dim is None else config.head_dim
        self.num_key_value_heads = config.num_key_value_heads
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.is_causal = True
        self.attention_dropout = config.attention_dropout

        """
            Grouped Differential Transformer. The group ratio is defined as origin_heads / noised_heads.
            Only integer ratios are allowed; in other words, origin_heads must be a multiple of noised_heads.
        """
        self.num_noise_heads = config.num_noise_heads
        self.grouped_ratio = (self.num_heads - self.num_noise_heads) // self.num_noise_heads
        self.q_heads = (self.grouped_ratio + 1) * self.num_noise_heads
        # Used only for motif-small, expanded from motif-tiny.
        self.expanded = getattr(config, "expanded", False)

        if (self.head_dim * self.num_heads) != self.hidden_size and not self.expanded:
            raise ValueError(
                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
                f" and `num_heads`: {self.num_heads})."
            )

        # re-init projections
        self.q_proj = nn.Linear(self.hidden_size, self.q_heads * self.head_dim, bias=False)
        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
        self.k_ratio = config.k_ratio
        k_noise_heads = self.num_key_value_heads // (self.k_ratio + 1)
        self.kv_repeat = self.num_noise_heads // k_noise_heads

        self.v_proj = nn.Linear(self.hidden_size, 2 * k_noise_heads * self.head_dim, bias=False)
        self.o_proj = nn.Linear(
            2 * self.grouped_ratio * self.num_noise_heads * self.head_dim, self.hidden_size, bias=False
        )

        # init lambdas
        for name in ["lambda_q1", "lambda_k1", "lambda_q2", "lambda_k2"]:
            setattr(self, name, nn.Parameter(torch.zeros(self.head_dim, dtype=torch.float32)))
            getattr(self, name).data.normal_(mean=0.0, std=0.1)

        # Uses same norm as motif norm, without elementwise_affine option
        RMSNorm = kernelRMSNorm if kernelRMSNorm is not None else MotifRMSNorm
        self.subln = RMSNorm(2 * self.head_dim, eps=1e-5)
        self.lambda_init = 0.8 - 0.6 * math.exp(-0.3 * (layer_idx - 1))

        self.rotary_emb = MotifRotaryEmbedding(
            self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, config=config,
        )

        self.fused_rope = getattr(config, "fused_rope", False)

    def _reshape_heads(self, tensor, grouped_ratio, num_groups):
        """2-way head split tensor reshape"""

        # split by num_heads, the stripe pattern is friendly to tensor parallel.
        tensor = einops.rearrange(
            tensor,
            "... (num_groups group_size) D -> ... num_groups group_size D",
            num_groups=num_groups,
            group_size=grouped_ratio + 1,
        )
        
        tensor1 = tensor[..., :grouped_ratio, :]
        tensor2 = tensor[..., grouped_ratio:, :]

        return tensor1.contiguous(), tensor2.contiguous()


    def _restore_shape(self, tensor, batch_size, seq_len):
        """restore tensor"""
        return tensor.reshape(batch_size, seq_len, -1, self.head_dim)


    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        assert self.grouped_ratio == 1, "Vanilla attention cannot be used when grouped_ratio > 1."

        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, -1).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(
            query_states, key_states, cos, sin, fused_rope=self.fused_rope
        )

        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        key_states = repeat_kv(key_states, 1, self.num_key_value_groups)
        value_states = repeat_kv(value_states, 1, self.num_key_value_groups)

        # repeat k/v heads if n_kv_heads < n_heads
        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)

        kv_seq_len = key_states.shape[-2]
        offset = kv_seq_len - q_len

        attention_mask = torch.triu(
            torch.full((q_len, kv_seq_len), float("-inf"), dtype=attn_weights.dtype, device=attn_weights.device),
            1 + offset,
        )

        attn_weights = attn_weights + attention_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)

        # differential transformer lambdas
        lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1).float()).type_as(attn_weights)
        lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1).float()).type_as(attn_weights)
        lambda_full = lambda_1 - lambda_2 + self.lambda_init
        attn_weights = attn_weights.view(bsz, self.num_heads, 2, q_len, -1)
        attn_weights = attn_weights[:, :, 0] - lambda_full * attn_weights[:, :, 1]

        attn_output = torch.matmul(attn_weights, value_states)
        attn_output = self.subln(attn_output)
        attn_output = attn_output * (1 - self.lambda_init)

        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim * 2):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)

        attn_output = self.o_proj(attn_output)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value


class MotifFlashAttention2(MotifAttention):
    """
    Motif flash attention module, following Motif attention module. This module inherits from `MotifAttention`
    as the weights of the module stays untouched. The only required change would be on the forward pass
    where it needs to correctly call the public API of flash attention and deal with padding tokens
    in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom
    config.max_window_layers layers.
    """

    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

    def _compute_attention(
        self,
        query_states,
        key_states,
        value_states,
        attention_mask,
        q_len,
        position_ids,
        dropout_rate,
        sliding_window,
    ):
        """Flash Attention 2 implements"""

        return _flash_attention_forward(
            query_states,
            key_states,
            value_states,
            attention_mask,
            q_len,
            position_ids=position_ids,
            dropout=dropout_rate,
            sliding_window=sliding_window,
            is_causal=self.is_causal,
            use_top_left_mask=self._flash_attn_uses_top_left_mask,
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
    ):
        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, -1, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        
        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(
            query_states, key_states, cos, sin, fused_rope=self.fused_rope
        )

        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        dropout_rate = 0.0 if not self.training else self.attention_dropout

        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
        # therefore the input hidden states gets silently casted in float32. Hence, we need
        # cast them back in float16 just to be sure everything works as expected.
        input_dtype = query_states.dtype
        if input_dtype == torch.float32:
            if torch.is_autocast_enabled():
                target_dtype = torch.get_autocast_gpu_dtype()
            # Handle the case where the model is quantized
            elif hasattr(self.config, "_pre_quantization_dtype"):
                target_dtype = self.config._pre_quantization_dtype
            else:
                target_dtype = self.q_proj.weight.dtype

            logger.warning_once(
                f"The input hidden states seems to be silently casted in float32, this might be related to"
                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
                f" {target_dtype}."
            )

            query_states = query_states.to(target_dtype)
            key_states = key_states.to(target_dtype)
            value_states = value_states.to(target_dtype)

        q_len = query_states.shape[-2]
        kv_seq_len = key_states.shape[-2]

        # Reashape to the expected shape for Flash Attention
        query_states = query_states.transpose(1, 2)
        key_states = key_states.transpose(1, 2)
        value_states = value_states.transpose(1, 2)

        if (
            self.config.use_sliding_window
            and getattr(self.config, "sliding_window", None) is not None
            and self.layer_idx >= self.config.max_window_layers
        ):
            sliding_window = self.config.sliding_window
        else:
            sliding_window = None

        num_groups = self.q_heads // (self.grouped_ratio + 1)
        q1, q2 = self._reshape_heads(query_states, self.grouped_ratio, num_groups)

        num_groups = self.num_key_value_heads // (self.k_ratio + 1)
        k1, k2 = self._reshape_heads(key_states, self.k_ratio, num_groups)
        v1, v2 = self._reshape_heads(value_states, 1, num_groups)

        q1, q2 = self._restore_shape(q1, bsz, q_len), self._restore_shape(q2, bsz, q_len)
        k1, k2 = self._restore_shape(k1, bsz, kv_seq_len), self._restore_shape(k2, bsz, kv_seq_len)
        v1, v2 = self._restore_shape(v1, bsz, kv_seq_len), self._restore_shape(v2, bsz, kv_seq_len)

        q_f = torch.cat([q1, q2], dim=2)

        k1 = repeat_kv(k1, 2, self.kv_repeat)
        k2 = repeat_kv(k2, 2, self.kv_repeat)
        v1 = repeat_kv(v1, 2, self.kv_repeat)
        v2 = repeat_kv(v2, 2, self.kv_repeat)
        
        if self.k_ratio == 1:
            k_f = torch.cat([repeat_kv(k1, 2, self.grouped_ratio), k2], dim=2)
        else:
            k_f = torch.cat([k1, k2], dim=2)
        v1_f = torch.cat([repeat_kv(v1, 2, self.grouped_ratio), v1], dim=2)
        v2_f = torch.cat([repeat_kv(v2, 2, self.grouped_ratio), v2], dim=2)

        attn_1, attn_2 = (
            self._compute_attention(
                q_f, k_f, v1_f, attention_mask, q_len, position_ids, dropout_rate, sliding_window
            ),
            self._compute_attention(
                q_f, k_f, v2_f, attention_mask, q_len, position_ids, dropout_rate, sliding_window
            ),
        )

        merged_attn = torch.cat([attn_1, attn_2], dim=-1)
        attn_o = merged_attn[..., :-num_groups, :]
        attn_n_group = merged_attn[..., -num_groups:, :]
        attn_n = repeat_kv(attn_n_group, 2, self.grouped_ratio)

        lambda_q1 = self.lambda_q1.unsqueeze(0).expand([bsz, self.lambda_q1.shape[0]])
        lambda_q2 = self.lambda_q2.unsqueeze(0).expand([bsz, self.lambda_q2.shape[0]])

        lambda_1 = torch.exp(torch.sum(lambda_q1 * self.lambda_k1, dim=-1).float()).type_as(attn_o)
        lambda_2 = torch.exp(torch.sum(lambda_q2 * self.lambda_k2, dim=-1).float()).type_as(attn_n)

        lambda_full = lambda_1 - lambda_2 + self.lambda_init

        attn_output = attn_o - lambda_full.view([bsz, 1, 1, 1]) * attn_n

        attn_output = self.subln(attn_output.float()).bfloat16()
        attn_output = attn_output * (1 - self.lambda_init)

        if attn_output.size() != (bsz, q_len, self.grouped_ratio * self.num_noise_heads, self.head_dim * 2):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.reshape(bsz, q_len, -1)
        attn_output = self.o_proj(attn_output)

        return attn_output, None, past_key_value


class MotifSdpaAttention(MotifAttention):
    """
    Motif attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
    `MotifAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
    SDPA API.
    """

    # Adapted from MotifAttention.forward
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        assert self.grouped_ratio == 1, "Scaled dot product attention cannot be used when grouped_ratio > 1."
        if output_attentions:
            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
            logger.warning_once(
                "MotifModel is using MotifSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
            )
            return super().forward(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                use_cache=use_cache,
            )

        bsz, q_len, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states)
        key_states = self.k_proj(hidden_states)
        value_states = self.v_proj(hidden_states)

        query_states = query_states.view(bsz, q_len, 2 * self.num_heads, self.head_dim).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, 2 * self.num_key_value_heads, self.head_dim).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, 2 * self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(
            query_states, key_states, cos, sin, fused_rope=self.fused_rope
        )

        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)

        dropout_rate = 0.0 if not self.training else self.attention_dropout

        q = query_states.transpose(1, 2)
        k = key_states.transpose(1, 2)
        v = value_states.transpose(1, 2)

        num_groups = self.q_heads // (self.grouped_ratio + 1)
        q1, q2 = self._reshape_heads(query_states, self.grouped_ratio, num_groups)

        num_groups = self.num_key_value_heads // (self.k_ratio + 1)
        k1, k2 = self._reshape_heads(key_states, self.k_ratio, num_groups)
        v1, v2 = self._reshape_heads(value_states, 1, num_groups)

        q1, q2 = self._restore_shape(q1, bsz, q_len), self._restore_shape(q2, bsz, q_len)
        k1, k2 = self._restore_shape(k1, bsz, kv_seq_len), self._restore_shape(k2, bsz, kv_seq_len)
        v1, v2 = self._restore_shape(v1, bsz, kv_seq_len), self._restore_shape(v2, bsz, kv_seq_len)

        q_f = torch.cat([q1, q2], dim=2)
        
        if self.k_ratio == 1:
            k_f = torch.cat([repeat_kv(k1, 2, self.grouped_ratio), k2], dim=2)
        else:
            k_f = torch.cat([k1, k2], dim=2)

        scale_factor = 1.0 / (self.head_dim**0.5)
        masked_bias = attention_mask.expand(bsz, self.q_heads, q_len, kv_seq_len)

        attn1 = ScaledDotProductAttention(
            q_f, k_f, v1_f, masked_bias, dropout_rate, self.training, scale_factor, False
        )
        attn2 = ScaledDotProductAttention(
            q_f, k_f, v2_f, masked_bias, dropout_rate, self.training, scale_factor, False
        )

        merged_attn = torch.cat([attn_1, attn_2], dim=-1)
        attn_o = merged_attn[..., :-num_groups, :]
        attn_n_group = merged_attn[..., -num_groups:, :]
        attn_n = repeat_kv(attn_n_group, 2, self.grouped_ratio)

        lambda_1 = torch.exp(torch.sum(self.lambda_q1 * self.lambda_k1, dim=-1).float()).type_as(q)
        lambda_2 = torch.exp(torch.sum(self.lambda_q2 * self.lambda_k2, dim=-1).float()).type_as(q)
        lambda_full = lambda_1 - lambda_2 + self.lambda_init
        attn_output = attn_o - lambda_full.view([bsz, 1, 1, 1]) * attn_n

        attn_output = self.subln(attn_output.float()).bfloat16()
        attn_output = attn_output * (1 - self.lambda_init)

        if attn_output.size() != (bsz, q_len, self.grouped_ratio * self.num_noise_heads, self.head_dim * 2):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.grouped_ratio * self.num_noise_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
        attn_output = self.o_proj(attn_output)

        return attn_output, None, past_key_value


MOTIF_ATTENTION_CLASSES = {
    "eager": MotifAttention,
    "flash_attention_2": MotifFlashAttention2,
    "sdpa": MotifSdpaAttention,
}


class MotifDecoderLayer(nn.Module):
    def __init__(self, config: MotifConfig, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        if config.sliding_window and config._attn_implementation != "flash_attention_2":
            logger.warning_once(
                f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
                "unexpected results may be encountered."
            )
        self.self_attn = MOTIF_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)

        self.mlp = MotifMLP(config)
        RMSNorm = kernelRMSNorm if kernelRMSNorm is not None else MotifRMSNorm
        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        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,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,  # will become mandatory in v4.46
        **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, sequence_length)` where padding elements are indicated by 0.
            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
            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
                Indices depicting the position of the input sequence tokens in the sequence.
            position_embeddings (`Tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
                with `head_dim` being the embedding dimension of each attention head.
            kwargs (`dict`, *optional*):
                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
                into the model
        """

        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states.float()).bfloat16()

        # Self Attention
        hidden_states, self_attn_weights, present_key_value = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states.float()).bfloat16()
        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


MOTIF_START_DOCSTRING = r"""
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`MotifConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""


@add_start_docstrings(
    "The bare Motif Model outputting raw hidden-states without any specific head on top.",
    MOTIF_START_DOCSTRING,
)
class MotifPreTrainedModel(PreTrainedModel):
    config_class = MotifConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["MotifDecoderLayer"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True
    _supports_quantized_cache = True
    _supports_static_cache = True

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data = torch.where(abs(module.weight.data) > 3 * std, 0, module.weight.data)
            if module.bias is not None:
                module.bias.data.zero_()

            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data = torch.where(abs(module.weight.data) > 3 * std, 0, module.weight.data)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()


MOTIF_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).

            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.

            [What are position IDs?](../glossary#position-ids)
        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.

            Two formats are allowed:
            - a [`~cache_utils.Cache`] instance, see our
            [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
            cache format.

            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
            legacy cache format will be returned.

            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
            of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        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`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
            the complete sequence length.
"""


@add_start_docstrings(
    "The bare Motif Model outputting raw hidden-states without any specific head on top.",
    MOTIF_START_DOCSTRING,
)
class MotifModel(MotifPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MotifDecoderLayer`]

    Args:
        config: MotifConfig
    """

    def __init__(self, config: MotifConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [MotifDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self._attn_implementation = config._attn_implementation
        RMSNorm = kernelRMSNorm if kernelRMSNorm is not None else MotifRMSNorm
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.head_dim = self.hidden_size // self.num_heads if config.head_dim is None else config.head_dim
        self.max_position_embeddings = config.max_position_embeddings
        self.rope_theta = config.rope_theta
        self.rotary_emb = MotifRotaryEmbedding(
            self.head_dim, max_position_embeddings=self.max_position_embeddings, base=self.rope_theta, config=config,
        )

        self.gradient_checkpointing = False
        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    @add_start_docstrings_to_model_forward(MOTIF_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = 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,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        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
        )
        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

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        # kept for BC (non `Cache` `past_key_values` inputs)
        return_legacy_cache = False
        if use_cache and not isinstance(past_key_values, Cache):
            return_legacy_cache = True
            if past_key_values is None:
                past_key_values = DynamicCache()
            else:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
                logger.warning_once(
                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
                    "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
                )

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

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
            )
        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = self._update_causal_mask(
            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
        )

        hidden_states = inputs_embeds
        bsz, q_len, _ = hidden_states.size()
        # create position embeddings to be shared across the decoder layers
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

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

        for decoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    causal_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                    cache_position,
                    position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
                    position_embeddings=position_embeddings,
                )

            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],)

        hidden_states = self.norm(hidden_states.float()).bfloat16()

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if return_legacy_cache:
            next_cache = next_cache.to_legacy_cache()

        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,
        )

    def _update_causal_mask(
        self,
        attention_mask: torch.Tensor,
        input_tensor: torch.Tensor,
        cache_position: torch.Tensor,
        past_key_values: Cache,
        output_attentions: bool,
    ):
        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and 0.0 in attention_mask:
                return attention_mask
            return None

        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
        # to infer the attention mask.
        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_static_cache = isinstance(past_key_values, StaticCache)
        using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)

        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
        if (
            self.config._attn_implementation == "sdpa"
            and not (using_static_cache or using_sliding_window_cache)
            and not output_attentions
        ):
            if AttentionMaskConverter._ignore_causal_mask_sdpa(
                attention_mask,
                inputs_embeds=input_tensor,
                past_key_values_length=past_seen_tokens,
                sliding_window=self.config.sliding_window,
                is_training=self.training,
            ):
                return None

        dtype, device = input_tensor.dtype, input_tensor.device
        min_dtype = torch.finfo(dtype).min
        sequence_length = input_tensor.shape[1]
        # SlidingWindowCache or StaticCache
        if using_sliding_window_cache or using_static_cache:
            target_length = past_key_values.get_max_cache_shape()
        # DynamicCache or no cache
        else:
            target_length = (
                attention_mask.shape[-1]
                if isinstance(attention_mask, torch.Tensor)
                else past_seen_tokens + sequence_length + 1
            )

        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
            attention_mask,
            sequence_length=sequence_length,
            target_length=target_length,
            dtype=dtype,
            device=device,
            cache_position=cache_position,
            batch_size=input_tensor.shape[0],
            config=self.config,
            past_key_values=past_key_values,
        )

        if (
            self.config._attn_implementation == "sdpa"
            and attention_mask is not None
            and attention_mask.device.type == "cuda"
            and not output_attentions
        ):
            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
            # Details: https://github.com/pytorch/pytorch/issues/110213
            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

        return causal_mask

    @staticmethod
    def _prepare_4d_causal_attention_mask_with_cache_position(
        attention_mask: torch.Tensor,
        sequence_length: int,
        target_length: int,
        dtype: torch.dtype,
        device: torch.device,
        cache_position: torch.Tensor,
        batch_size: int,
        config: MotifConfig,
        past_key_values: Cache,
    ):
        """
        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.

        Args:
            attention_mask (`torch.Tensor`):
                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
            sequence_length (`int`):
                The sequence length being processed.
            target_length (`int`):
                The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
            dtype (`torch.dtype`):
                The dtype to use for the 4D attention mask.
            device (`torch.device`):
                The device to plcae the 4D attention mask on.
            cache_position (`torch.Tensor`):
                Indices depicting the position of the input sequence tokens in the sequence.
            batch_size (`torch.Tensor`):
                Batch size.
            config (`MotifConfig`):
                The model's configuration class
            past_key_values (`Cache`):
                The cache class that is being used currently to generate
        """
        if attention_mask is not None and attention_mask.dim() == 4:
            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
            causal_mask = attention_mask
        else:
            min_dtype = torch.finfo(dtype).min
            causal_mask = torch.full(
                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
            )
            diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
                -1, 1
            )
            if config.sliding_window is not None:
                # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
                # the check is needed to verify is current checkpoint was trained with sliding window or not
                if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
                    sliding_attend_mask = torch.arange(target_length, device=device) <= (
                        cache_position.reshape(-1, 1) - config.sliding_window
                    )
                    diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
            causal_mask *= diagonal_attend_mask
            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
            if attention_mask is not None:
                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                if attention_mask.shape[-1] > target_length:
                    attention_mask = attention_mask[:, :target_length]
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
                padding_mask = padding_mask == 0
                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                    padding_mask, min_dtype
                )
        return causal_mask


class MotifForCausalLM(MotifPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = MotifModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

        if config.tie_word_embeddings:
            self.tie_weights()

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    @add_start_docstrings_to_model_forward(MOTIF_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = 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,
        cache_position: Optional[torch.LongTensor] = None,
        num_logits_to_keep: int = 0,
        **loss_kwargs,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        Args:
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

            num_logits_to_keep (`int`, *optional*):
                Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
                `input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
                token can save memory, which becomes pretty significant for long sequences or large vocabulary size.

        Returns:

        Example:

        ```python
        >>> from transformers import AutoTokenizer, MotifForCausalLM

        >>> model = MotifForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

        >>> prompt = "Hey, are you conscious? Can you talk to me?"
        >>> inputs = tokenizer(prompt, return_tensors="pt")

        >>> # Generate
        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
        ```"""

        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)
        outputs = self.model(
            input_ids=input_ids,
            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,
            cache_position=cache_position,
        )

        hidden_states = outputs[0]
        hidden_states = hidden_states
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        logits = self.lm_head(hidden_states[:, -num_logits_to_keep:, :])
        logits = logits.float()

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Enable model parallelism
            shift_labels = shift_labels.to(shift_logits.device)
            loss = loss_fct(shift_logits, shift_labels)

        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,
        )