modeling_hinvec.py

from typing import Callable, Optional

import math

import torch
from torch import nn

from transformers.activations import ACT2FN
from transformers.integrations import use_kernel_forward_from_hub
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_layers import GradientCheckpointingLayer
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from transformers.processing_utils import Unpack
from transformers.utils import TransformersKwargs, auto_docstring
from transformers.utils.generic import check_model_inputs
from .configuration_hinvec import HinvecConfig

SUPPORTED_SLIDING_BACKENDS=["flash_attention_3", "flash_attention_2"]
class HinvecMLP(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        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, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj


def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors."""
    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)
    return q_embed, k_embed


def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
    """
    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
    """
    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
    if n_rep == 1:
        return hidden_states
    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)


def eager_attention_forward(
    module: nn.Module,
    query: torch.Tensor,
    key: torch.Tensor,
    value: torch.Tensor,
    attention_mask: Optional[torch.Tensor],
    scaling: float,
    dropout: float = 0.0,
    **kwargs: Unpack[TransformersKwargs],
):
    """Standard eager attention without sliding window optimization."""
    key_states = repeat_kv(key, 1)
    value_states = repeat_kv(value, 1)

    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling

    if attention_mask is not None:
        attention_mask = attention_mask[:, None, None, :]
        attn_weights = attn_weights + attention_mask

    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
    attn_output = torch.matmul(attn_weights, value_states)
    attn_output = attn_output.transpose(1, 2).contiguous()

    return attn_output, attn_weights


class HinvecAttention(nn.Module):
    def __init__(self, config, layer_idx):
        super().__init__()

        self.config = config
        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.scaling = self.head_dim**-0.5
        self.dropout = config.attention_dropout
        self.is_causal = False

        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
                f"heads ({config.num_attention_heads})"
            )
        self.num_heads = config.num_attention_heads
        self.head_dim = int(config.hidden_size / config.num_attention_heads)
        self.embed_dim = config.hidden_size

        self.query = nn.Linear(config.hidden_size, self.embed_dim)
        self.key = nn.Linear(config.hidden_size, self.embed_dim)
        self.value = nn.Linear(config.hidden_size, self.embed_dim)

        self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True)

        self.layer_idx = layer_idx
        
        # Initialize sliding_window and sliding_window
        self.sliding_window = None
        
        if config.layer_types[layer_idx] == "sliding_attention":
            self.sliding_window = config.sliding_window
            assert self.sliding_window % 2 == 0, (
                f"`sliding_window` for layer {self.layer_idx} has to be an even value. Given {self.sliding_window}"
            )
            assert self.sliding_window > 0, (
                f"`sliding_window` for layer {self.layer_idx} has to be positive. Given {self.sliding_window}"
            )

            self.one_sided_attn_window_size = self.sliding_window // 2

        # Store attention_dropout as instance variable
        self.attention_dropout = config.attention_dropout

    def forward(
        self,
        hidden_states,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask=None,
        position_ids: Optional[torch.LongTensor] = None,
        is_index_masked=None,
        output_attentions=False,
        **kwargs,
    ):
        """
        [`HinvecAttention`] expects *len(hidden_states)* to be multiple of *sliding_window*. Padding to
        *sliding_window* happens in [`LongformerModel.forward`] to avoid redoing the padding on each layer.

        The *attention_mask* is changed in [`LongformerModel.forward`] from 0, 1, 2 to:

            - -10000: no attention
            - 0: local attention
        """
        print(self.layer_idx)
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        # project hidden states
        query_vectors = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
        key_vectors = self.key(hidden_states).view(hidden_shape).transpose(1, 2)
        value_vectors = self.value(hidden_states).view(hidden_shape).transpose(1, 2)

        batch_size, seq_len, embed_dim = hidden_states.size()
        assert embed_dim == self.embed_dim, (
            f"hidden_states should have embed_dim = {self.embed_dim}, but has {embed_dim}"
        )

        cos, sin = position_embeddings
        query_vectors, key_vectors = apply_rotary_pos_emb(query_vectors, key_vectors, cos, sin)

        if self.sliding_window and self.config._attn_implementation == "eager":
            # normalize query
            query_vectors /= math.sqrt(self.head_dim)

            query_vectors = query_vectors.transpose(1, 2)
            key_vectors = key_vectors.transpose(1, 2)

            attn_scores = self._sliding_chunks_query_key_matmul(
                query_vectors, key_vectors, self.one_sided_attn_window_size
            )

            # values to pad for attention probs
            remove_from_windowed_attention_mask = (attention_mask != 0)[:, :, None, None]

            # cast to fp32/fp16 then replace 1's with -inf
            float_mask = remove_from_windowed_attention_mask.type_as(query_vectors).masked_fill(
                remove_from_windowed_attention_mask, torch.finfo(query_vectors.dtype).min
            )
            # diagonal mask with zeros everywhere and -inf inplace of padding
            diagonal_mask = self._sliding_chunks_query_key_matmul(
                float_mask.new_ones(size=float_mask.size()), float_mask, self.one_sided_attn_window_size
            )

            # pad local attention probs
            attn_scores += diagonal_mask

            assert list(attn_scores.size()) == [
                batch_size,
                seq_len,
                self.num_heads,
                self.one_sided_attn_window_size * 2 + 1,
            ], (
                f"local_attn_probs should be of size ({batch_size}, {seq_len}, {self.num_heads},"
                f" {self.one_sided_attn_window_size * 2 + 1}), but is of size {attn_scores.size()}"
            )

            attn_probs = nn.functional.softmax(
                attn_scores, dim=-1, dtype=torch.float32
            )  # use fp32 for numerical stability

            # softmax sometimes inserts NaN if all positions are masked, replace them with 0
            attn_probs = torch.masked_fill(attn_probs, is_index_masked[:, :, None, None], 0.0)
            attn_probs = attn_probs.type_as(attn_scores)

            # free memory
            del attn_scores

            # apply dropout
            attn_probs = nn.functional.dropout(attn_probs, p=self.dropout, training=self.training)

            value_vectors = value_vectors.transpose(1, 2)

            # compute local attn only
            attn_output = self._sliding_chunks_matmul_attn_probs_value(
                attn_probs, value_vectors, self.one_sided_attn_window_size
            )

            assert attn_output.size() == (batch_size, seq_len, self.num_heads, self.head_dim), "Unexpected size"
            attn_output = attn_output.transpose(0, 1).reshape(seq_len, batch_size, embed_dim).contiguous()

            attn_weights = attn_probs.transpose(1, 2)

        elif self.sliding_window:

            if self.config._attn_implementation in SUPPORTED_SLIDING_BACKENDS:
                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
                attn_output, attn_weights = attention_interface(
                    self,
                    query_vectors,
                    key_vectors,
                    value_vectors,
                    (attention_mask < 0)*torch.finfo(query_vectors.dtype).min,
                    dropout=0.0 if not self.training else self.attention_dropout,
                    scaling=self.scaling,
                    sliding_window=self.sliding_window,
                    **kwargs,
                )

            else:
                # Construct sliding window mask
                seq_len = query_vectors.size(-2)
                sw_mask = self._build_sliding_window_mask(seq_len, query_vectors.device, query_vectors.dtype)
                print(sw_mask)

                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

                attn_output, attn_weights = attention_interface(
                    self,
                    query_vectors,
                    key_vectors,
                    value_vectors,
                    # Combine with user-provided mask by addition
                    (attention_mask < 0) * torch.finfo(query_vectors.dtype).min + sw_mask,
                    dropout=0.0 if not self.training else self.attention_dropout,
                    scaling=self.scaling,
                    is_causal=False,  # IMPORTANT
                    **kwargs,
                )

        else:
            # Use standard attention implementation
            attention_interface: Callable = eager_attention_forward
            if self.config._attn_implementation != "eager":
                attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

            attn = (attention_mask < 0)*torch.finfo(query_vectors.dtype).min
            attn = attn[:, None, :, None].expand(-1, 1, attn.size(1), attn.size(1))
            attn_output, attn_weights = attention_interface(
                self,
                query_vectors,
                key_vectors,
                value_vectors,
                attn,
                dropout=0.0 if not self.training else self.attention_dropout,
                scaling=self.scaling,
                **kwargs,
            )

        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights

    def _build_sliding_window_mask(self, seq_len, device, dtype):
        """
        Creates an additive attention mask of shape (1, 1, seq_len, seq_len)
        where each token attends only to a window around itself.
        Allowed positions: i-w ... i+w
        Others masked with -∞
        """
        w = self.one_sided_attn_window_size
        # Base mask: True means "mask this (disallowed)"
        mask = torch.ones(seq_len, seq_len, device=device, dtype=torch.bool)

        # Fill allowed window as False
        arange = torch.arange(seq_len, device=device)
        for i in range(seq_len):
            left = max(0, i - w)
            right = min(seq_len, i + w + 1)
            mask[i, left:right] = False

        # Convert boolean mask → additive logit mask
        # True → -inf, False → 0
        mask = mask.masked_fill(mask, torch.finfo(dtype).min)

        # Shape to (batch, heads, Q, K) broadcastable format
        return mask.view(1, 1, seq_len, seq_len)
    
    @staticmethod
    def _pad_and_transpose_last_two_dims(hidden_states_padded, padding):
        """pads rows and then flips rows and columns"""
        hidden_states_padded = nn.functional.pad(
            hidden_states_padded, padding
        )  # padding value is not important because it will be overwritten
        hidden_states_padded = hidden_states_padded.view(
            *hidden_states_padded.size()[:-2], hidden_states_padded.size(-1), hidden_states_padded.size(-2)
        )
        return hidden_states_padded

    @staticmethod
    def _pad_and_diagonalize(chunked_hidden_states):
        """
        shift every row 1 step right, converting columns into diagonals.

        Example:

        ```python
        chunked_hidden_states: [
            0.4983,
            2.6918,
            -0.0071,
            1.0492,
            -1.8348,
            0.7672,
            0.2986,
            0.0285,
            -0.7584,
            0.4206,
            -0.0405,
            0.1599,
            2.0514,
            -1.1600,
            0.5372,
            0.2629,
        ]
        window_overlap = num_rows = 4
        ```

                     (pad & diagonalize) => [ 0.4983, 2.6918, -0.0071, 1.0492, 0.0000, 0.0000, 0.0000
                       0.0000, -1.8348, 0.7672, 0.2986, 0.0285, 0.0000, 0.0000 0.0000, 0.0000, -0.7584, 0.4206,
                       -0.0405, 0.1599, 0.0000 0.0000, 0.0000, 0.0000, 2.0514, -1.1600, 0.5372, 0.2629 ]
        """
        total_num_heads, num_chunks, window_overlap, hidden_dim = chunked_hidden_states.size()
        chunked_hidden_states = nn.functional.pad(
            chunked_hidden_states, (0, window_overlap + 1)
        )  # total_num_heads x num_chunks x window_overlap x (hidden_dim+window_overlap+1). Padding value is not important because it'll be overwritten
        chunked_hidden_states = chunked_hidden_states.view(
            total_num_heads, num_chunks, -1
        )  # total_num_heads x num_chunks x window_overlap*window_overlap+window_overlap
        chunked_hidden_states = chunked_hidden_states[
            :, :, :-window_overlap
        ]  # total_num_heads x num_chunks x window_overlap*window_overlap
        chunked_hidden_states = chunked_hidden_states.view(
            total_num_heads, num_chunks, window_overlap, window_overlap + hidden_dim
        )
        chunked_hidden_states = chunked_hidden_states[:, :, :, :-1]
        return chunked_hidden_states

    @staticmethod
    def _chunk(hidden_states, window_overlap, onnx_export: bool = False):
        """convert into overlapping chunks. Chunk size = 2w, overlap size = w"""
        if not onnx_export:
            # non-overlapping chunks of size = 2w
            hidden_states = hidden_states.view(
                hidden_states.size(0),
                torch.div(hidden_states.size(1), (window_overlap * 2), rounding_mode="trunc"),
                window_overlap * 2,
                hidden_states.size(2),
            )
            # use `as_strided` to make the chunks overlap with an overlap size = window_overlap
            chunk_size = list(hidden_states.size())
            chunk_size[1] = chunk_size[1] * 2 - 1

            chunk_stride = list(hidden_states.stride())
            chunk_stride[1] = chunk_stride[1] // 2
            return hidden_states.as_strided(size=chunk_size, stride=chunk_stride)

        # When exporting to ONNX, use this separate logic
        # have to use slow implementation since as_strided, unfold and 2d-tensor indexing aren't supported (yet) in ONNX export

        # TODO replace this with
        # > return hidden_states.unfold(dimension=1, size=window_overlap * 2, step=window_overlap).transpose(2, 3)
        # once `unfold` is supported
        # the case hidden_states.size(1) == window_overlap * 2 can also simply return hidden_states.unsqueeze(1), but that's control flow

        chunk_size = [
            hidden_states.size(0),
            torch.div(hidden_states.size(1), window_overlap, rounding_mode="trunc") - 1,
            window_overlap * 2,
            hidden_states.size(2),
        ]

        overlapping_chunks = torch.empty(chunk_size, device=hidden_states.device)
        for chunk in range(chunk_size[1]):
            overlapping_chunks[:, chunk, :, :] = hidden_states[
                :, chunk * window_overlap : chunk * window_overlap + 2 * window_overlap, :
            ]
        return overlapping_chunks

    @staticmethod
    def _mask_invalid_locations(input_tensor, affected_seq_len) -> torch.Tensor:
        beginning_mask_2d = input_tensor.new_ones(affected_seq_len, affected_seq_len + 1).tril().flip(dims=[0])
        beginning_mask = beginning_mask_2d[None, :, None, :]
        ending_mask = beginning_mask.flip(dims=(1, 3))
        beginning_input = input_tensor[:, :affected_seq_len, :, : affected_seq_len + 1]
        beginning_mask = beginning_mask.expand(beginning_input.size())
        input_tensor[:, :affected_seq_len, :, : affected_seq_len + 1] = torch.full_like(
            beginning_input, -float("inf")
        ).where(beginning_mask.bool(), beginning_input)
        ending_input = input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :]
        ending_mask = ending_mask.expand(ending_input.size())
        input_tensor[:, -affected_seq_len:, :, -(affected_seq_len + 1) :] = torch.full_like(
            ending_input, -float("inf")
        ).where(ending_mask.bool(), ending_input)

    def _sliding_chunks_query_key_matmul(self, query: torch.Tensor, key: torch.Tensor, window_overlap: int):
        """
        Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
        implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer) with an
        overlap of size window_overlap
        """
        batch_size, seq_len, num_heads, head_dim = query.size()
        assert seq_len % (window_overlap * 2) == 0, (
            f"Sequence length should be multiple of {window_overlap * 2}. Given {seq_len}"
        )
        assert query.size() == key.size()

        chunks_count = torch.div(seq_len, window_overlap, rounding_mode="trunc") - 1
        # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size window_overlap * 2
        query = query.reshape(batch_size * num_heads, seq_len, head_dim)
        key = key.reshape(batch_size * num_heads, seq_len, head_dim)

        query = self._chunk(query, window_overlap, getattr(self.config, "onnx_export", False))
        key = self._chunk(key, window_overlap, getattr(self.config, "onnx_export", False))

        # matrix multiplication
        # bcxd: batch_size * num_heads x chunks x 2window_overlap x head_dim
        # bcyd: batch_size * num_heads x chunks x 2window_overlap x head_dim
        # bcxy: batch_size * num_heads x chunks x 2window_overlap x 2window_overlap
        diagonal_chunked_attention_scores = torch.einsum("bcxd,bcyd->bcxy", (query, key))  # multiply

        # convert diagonals into columns
        diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(
            diagonal_chunked_attention_scores, padding=(0, 0, 0, 1)
        )

        # allocate space for the overall attention matrix where the chunks are combined. The last dimension
        # has (window_overlap * 2 + 1) columns. The first (window_overlap) columns are the window_overlap lower triangles (attention from a word to
        # window_overlap previous words). The following column is attention score from each word to itself, then
        # followed by window_overlap columns for the upper triangle.

        diagonal_attention_scores = diagonal_chunked_attention_scores.new_zeros(
            (batch_size * num_heads, chunks_count + 1, window_overlap, window_overlap * 2 + 1)
        )

        # copy parts from diagonal_chunked_attention_scores into the combined matrix of attentions
        # - copying the main diagonal and the upper triangle
        diagonal_attention_scores[:, :-1, :, window_overlap:] = diagonal_chunked_attention_scores[
            :, :, :window_overlap, : window_overlap + 1
        ]
        diagonal_attention_scores[:, -1, :, window_overlap:] = diagonal_chunked_attention_scores[
            :, -1, window_overlap:, : window_overlap + 1
        ]
        # - copying the lower triangle
        diagonal_attention_scores[:, 1:, :, :window_overlap] = diagonal_chunked_attention_scores[
            :, :, -(window_overlap + 1) : -1, window_overlap + 1 :
        ]

        diagonal_attention_scores[:, 0, 1:window_overlap, 1:window_overlap] = diagonal_chunked_attention_scores[
            :, 0, : window_overlap - 1, 1 - window_overlap :
        ]

        # separate batch_size and num_heads dimensions again
        diagonal_attention_scores = diagonal_attention_scores.view(
            batch_size, num_heads, seq_len, 2 * window_overlap + 1
        ).transpose(2, 1)


        self._mask_invalid_locations(diagonal_attention_scores, window_overlap)
        return diagonal_attention_scores

    def _sliding_chunks_matmul_attn_probs_value(
        self, attn_probs: torch.Tensor, value: torch.Tensor, window_overlap: int
    ):
        """
        Same as _sliding_chunks_query_key_matmul but for attn_probs and value tensors. Returned tensor will be of the
        same shape as `attn_probs`
        """
        batch_size, seq_len, num_heads, head_dim = value.size()
        assert seq_len % (window_overlap * 2) == 0
        assert attn_probs.size()[:3] == value.size()[:3]
        assert attn_probs.size(3) == 2 * window_overlap + 1
        chunks_count = torch.div(seq_len, window_overlap, rounding_mode="trunc") - 1
        # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size 2 window overlap

        chunked_attn_probs = attn_probs.transpose(1, 2).reshape(
            batch_size * num_heads,
            torch.div(seq_len, window_overlap, rounding_mode="trunc"),
            window_overlap,
            2 * window_overlap + 1,
        )

        # group batch_size and num_heads dimensions into one
        value = value.transpose(1, 2).reshape(batch_size * num_heads, seq_len, head_dim)

        # pad seq_len with w at the beginning of the sequence and another window overlap at the end
        padded_value = nn.functional.pad(value, (0, 0, window_overlap, window_overlap), value=-1)

        # chunk padded_value into chunks of size 3 window overlap and an overlap of size window overlap
        chunked_value_size = (batch_size * num_heads, chunks_count + 1, 3 * window_overlap, head_dim)
        chunked_value_stride = padded_value.stride()
        chunked_value_stride = (
            chunked_value_stride[0],
            window_overlap * chunked_value_stride[1],
            chunked_value_stride[1],
            chunked_value_stride[2],
        )
        chunked_value = padded_value.as_strided(size=chunked_value_size, stride=chunked_value_stride)

        chunked_attn_probs = self._pad_and_diagonalize(chunked_attn_probs)

        context = torch.einsum("bcwd,bcdh->bcwh", (chunked_attn_probs, chunked_value))
        return context.view(batch_size, num_heads, seq_len, head_dim).transpose(1, 2)


@use_kernel_forward_from_hub("RMSNorm")
class HinvecRMSNorm(nn.Module):
    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
        """
        HinvecRMSNorm is equivalent to T5LayerNorm
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        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 HinvecEncoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: HinvecConfig, layer_idx: int):
        super().__init__()
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size

        self.self_attn = HinvecAttention(config=config, layer_idx=layer_idx)

        self.mlp = HinvecMLP(config)
        self.input_layernorm = HinvecRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = HinvecRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.attention_type = config.layer_types[layer_idx]

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        
        is_index_masked = attention_mask < 0

        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        # Self Attention
        hidden_states, _ = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            position_ids=position_ids,
            position_embeddings=position_embeddings,
            is_index_masked=is_index_masked,
            **kwargs,
        )
        hidden_states = residual + hidden_states

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


@auto_docstring
class HinvecPreTrainedModel(PreTrainedModel):
    config: HinvecConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["HinvecEncoderLayer"]
    _supports_flash_attn = True
    _supports_sdpa = True
    _supports_flex_attn = True

    _can_compile_fullgraph = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "hidden_states": HinvecEncoderLayer,
        "attentions": HinvecAttention,
    }


class HinvecRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor

    def __init__(self, config: HinvecConfig, device=None):
        super().__init__()
        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
            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.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq

    @torch.no_grad()
    @dynamic_rope_update
    def forward(self, x, position_ids):
        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
        position_ids_expanded = position_ids[:, None, :].float()

        device_type = x.device.type if isinstance(x.device.type, str) and x.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() * self.attention_scaling
            sin = emb.sin() * self.attention_scaling

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


@auto_docstring
class HinvecModel(HinvecPreTrainedModel):

    config_class = HinvecConfig

    def __init__(self, config: HinvecConfig):
        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(
            [HinvecEncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = HinvecRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = HinvecRotaryEmbedding(config=config)
        self.gradient_checkpointing = False
        self.has_sliding_layers = "sliding_attention" in self.config.layer_types

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

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def _pad_to_window_size(
        self,
        input_ids: torch.Tensor,
        attention_mask: torch.Tensor,
        token_type_ids: torch.Tensor,
        position_ids: torch.Tensor,
        inputs_embeds: torch.Tensor,
        pad_token_id: int,
    ):
        """A helper function to pad tokens and mask to work with implementation of Longformer self-attention."""
        # padding
        sliding_window = (
            self.config.sliding_window
            if isinstance(self.config.sliding_window, int)
            else max(self.config.sliding_window)
        )

        assert sliding_window % 2 == 0, f"`sliding_window` should be an even value. Given {sliding_window}"
        input_shape = input_ids.shape if input_ids is not None else inputs_embeds.shape
        batch_size, seq_len = input_shape[:2]

        padding_len = (sliding_window - seq_len % sliding_window) % sliding_window

        # this path should be recorded in the ONNX export, it is fine with padding_len == 0 as well
        if padding_len > 0:
            logger.warning_once(
                f"Input ids are automatically padded to be a multiple of `config.sliding_window`: {sliding_window}"
            )
            if input_ids is not None:
                input_ids = nn.functional.pad(input_ids, (0, padding_len), value=pad_token_id)
            if position_ids is not None:
                # pad with position_id = pad_token_id as in modeling_roberta.RobertaEmbeddings
                position_ids = nn.functional.pad(position_ids, (0, padding_len), value=pad_token_id)
            if inputs_embeds is not None:
                input_ids_padding = inputs_embeds.new_full(
                    (batch_size, padding_len),
                    self.config.pad_token_id,
                    dtype=torch.long,
                )
                inputs_embeds_padding = self.embeddings(input_ids_padding)
                inputs_embeds = torch.cat([inputs_embeds, inputs_embeds_padding], dim=-2)

            attention_mask = nn.functional.pad(
                attention_mask, (0, padding_len), value=0
            )  # no attention on the padding tokens
            token_type_ids = nn.functional.pad(token_type_ids, (0, padding_len), value=0)  # pad with token_type_id = 0

        return padding_len, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds

    @check_model_inputs
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> BaseModelOutputWithPast:
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

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

        device = inputs_embeds.device
        batch_size, seq_len = inputs_embeds.shape[:2]

        if position_ids is None:
            position_ids = torch.arange(seq_len, device=device).unsqueeze(0)
            position_ids = position_ids.expand(batch_size, -1)

        # 1. ----- CREATE ATTENTION MASK -----
        if attention_mask is None:
            attention_mask = torch.ones((batch_size, seq_len), device=device, dtype=torch.long)

        # 2. ----- PAD TO WINDOW SIZE -----
        sliding_window = (
            self.config.sliding_window
            if isinstance(self.config.sliding_window, int)
            else max(self.config.sliding_window)
        )
        
        padding_len = (sliding_window - seq_len % sliding_window) % sliding_window
        
        if padding_len > 0:
            # Pad input_ids if available
            if input_ids is not None:
                input_ids = torch.nn.functional.pad(
                    input_ids, (0, padding_len), value=self.config.pad_token_id
                )
            
            # Pad position_ids
            position_ids = torch.nn.functional.pad(
                position_ids, (0, padding_len), value=self.config.pad_token_id
            )
            
            # Pad inputs_embeds
            input_ids_padding = inputs_embeds.new_full(
                (batch_size, padding_len),
                self.config.pad_token_id,
                dtype=torch.long,
            )
            inputs_embeds_padding = self.embed_tokens(input_ids_padding)
            inputs_embeds = torch.cat([inputs_embeds, inputs_embeds_padding], dim=-2)
            
            # Pad attention_mask with 0s (no attention on padding)
            attention_mask = torch.nn.functional.pad(
                attention_mask, (0, padding_len), value=0
            )

        # 3. ----- CREATE PADDING MASK -----
        # Convert tri-level mask to continuous mask for attention
        # Tokens with attention_mask == 0 get -inf, others get 0
        expanded_mask = (attention_mask > 0).to(inputs_embeds.dtype)
        padding_mask = (1.0 - expanded_mask) * torch.finfo(inputs_embeds.dtype).min

        hidden_states = inputs_embeds

        # Create position embeddings to be shared across the decoder layers
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        for encoder_layer in self.layers:
            hidden_states = encoder_layer(
                hidden_states,
                attention_mask=padding_mask,
                position_ids=position_ids,
                position_embeddings=position_embeddings,
                **kwargs,
            )

        hidden_states = self.norm(hidden_states)
        
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
        )


__all__ = [
    "HinvecPreTrainedModel",
    "HinvecModel",
    "HinvecRMSNorm"
]