modeling_gear.py

from collections.abc import Callable
from typing import Optional, Union, Any

import math
import copy

import torch
import torch.nn.functional as F
from torch import nn

from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache
from transformers.generation import GenerationMixin
from transformers.masking_utils import create_causal_mask, create_sliding_window_causal_mask
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_layers import (
    GenericForQuestionAnswering,
    GenericForSequenceClassification,
    GenericForTokenClassification,
    GradientCheckpointingLayer,
)
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
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, can_return_tuple
from transformers.utils.generic import check_model_inputs
from transformers.utils.import_utils import is_causal_conv1d_available
from .configuration_gear import GearConfig

if is_causal_conv1d_available():
    from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
else:
    causal_conv1d_fn, causal_conv1d_update = None, None
    
kernel_modules = (causal_conv1d_fn, causal_conv1d_update)
is_fast_path_available = all(kernel_modules)


class GearHybridKVConvCache:
    """
    Attention and conv cache for Gear.

    It stores the Key and Value states as a list of tensors, one for each layer.
    Attention layer cache shape: `[batch_size, num_heads, seq_len, head_dim]`.
    Key-Value Conv layer cache shape: `[batch_size, hidden_size, L_cache-1]`.
    """

    # Override @property existing in Cache
    max_batch_size = None
    is_compileable = False
    key_cache = None
    value_cache = None

    def __init__(
        self,
        config: GearConfig,
        max_batch_size: int,
        dtype: torch.dtype = torch.float32,
        device: Union[torch.device, str, None] = None,
    ):
        self.key_cache = []
        self.value_cache = []
        self.max_batch_size = max_batch_size
        self.layer_types = config.layer_types
        self.first_attention_layer = self.layer_types.index("full_attention")
        self.conv_L_cache = config.conv_L_cache
        self._dtype = dtype

        self.key_conv_cache: list[torch.Tensor] = []
        self.value_conv_cache: list[torch.Tensor] = []
        device = torch.device(device) if device is not None else None
        
        for _ in range(config.num_hidden_layers):
            key_conv_state = torch.zeros(
                self.max_batch_size,
                config.num_key_value_heads * config.head_dim,
                self.conv_L_cache,
                dtype=self._dtype,
                device=device,
            )
            value_conv_state = torch.zeros_like(key_conv_state)
        
            torch._dynamo.mark_static_address(key_conv_state)
            torch._dynamo.mark_static_address(value_conv_state)
        
            self.key_conv_cache.append(key_conv_state)
            self.value_conv_cache.append(value_conv_state)
            

    def update(
        self,
        key_states: torch.Tensor,
        value_states: torch.Tensor,
        layer_idx: int,
        cache_kwargs: Optional[dict[str, Any]] = None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """
        Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.

        Parameters:
            key_states (`torch.Tensor`):
                The new key states to cache.
            value_states (`torch.Tensor`):
                The new value states to cache.
            layer_idx (`int`):
                The index of the layer to cache the states for.
            cache_kwargs (`Dict[str, Any]`, `optional`):
                Additional arguments for the cache subclass. No additional arguments are used in `DynamicCache`.

        Return:
            A tuple containing the updated key and value states.
        """
        # Update the cache
        if key_states is not None:
            if len(self.key_cache) <= layer_idx:
                # There may be skipped layers, fill them with empty lists
                for _ in range(len(self.key_cache), layer_idx):
                    self.key_cache.append(torch.tensor([]))
                    self.value_cache.append(torch.tensor([]))
                self.key_cache.append(key_states)
                self.value_cache.append(value_states)
            elif (
                not self.key_cache[layer_idx].numel()  # prefers not t.numel() to len(t) == 0 to export the model
            ):  # fills previously skipped layers; checking for tensor causes errors
                self.key_cache[layer_idx] = key_states
                self.value_cache[layer_idx] = value_states
            else:
                self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
                self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)

        return self.key_cache[layer_idx], self.value_cache[layer_idx]

    def reorder_cache(self, beam_idx: torch.LongTensor):
        """Reorders the cache for beam search, given the selected beam indices."""
        for layer_idx in range(len(self.key_cache)):
            device = self.key_cache[layer_idx].device
            self.key_cache[layer_idx] = self.key_cache[layer_idx].index_select(0, beam_idx.to(device))
            device = self.value_cache[layer_idx].device
            self.value_cache[layer_idx] = self.value_cache[layer_idx].index_select(0, beam_idx.to(device))

            device = self.key_conv_cache[layer_idx].device
            self.key_conv_cache[layer_idx] = self.key_conv_cache[layer_idx].index_select(0, beam_idx.to(device))
            device = self.value_conv_cache[layer_idx].device
            self.value_conv_cache[layer_idx] = self.value_conv_cache[layer_idx].index_select(0, beam_idx.to(device))

    def get_seq_length(self, layer_idx: Optional[int] = 0) -> int:
        """Returns the sequence length of the cached states. A layer index can be optionally passed."""
        # take any layer that contains cache and not empty tensor
        layer_idx = self.first_attention_layer if self.layer_types[layer_idx] != "full_attention" else layer_idx
        if len(self.key_cache) <= layer_idx or self.key_cache[layer_idx].numel() == 0:
            return 0
        return self.key_cache[layer_idx].shape[-2]

    def get_mask_sizes(self, cache_position: torch.Tensor, layer_idx: int) -> tuple[int, int]:
        """
        Return a tuple (kv_length, kv_offset) corresponding to the length and offset that will be returned for
        the given layer at `layer_idx`.
        The masks are then prepared according to the given lengths (kv_length, kv_offset) and patterns (i.e. sliding_window, chunk_size),
        for each layer.
        """
        full_mask_kv_offset = 0
        query_length = cache_position.shape[0]
        past_seen_tokens = self.get_seq_length()
        kv_length = query_length + past_seen_tokens
        return kv_length, full_mask_kv_offset

    def crop(self, max_length: int):
        """Crop the cache to the given length"""
        if max_length < 0:
            max_length = self.get_seq_length() - abs(max_length)

        if self.get_seq_length() <= max_length:
            return

        for idx in range(len(self.key_cache)):
            if self.key_cache[idx].numel():
                self.key_cache[idx] = self.key_cache[idx][..., :max_length, :]
                self.value_cache[idx] = self.value_cache[idx][..., :max_length, :]

    def __len__(self) -> int:
        return len(self.key_cache)

    def __getitem__(self, layer_idx: int) -> tuple[torch.Tensor, torch.Tensor]:
        return self.key_cache[layer_idx], self.value_cache[layer_idx]

    def reset(self):
        for layer_idx in range(len(self.key_conv_cache)):
            self.key_conv_cache[layer_idx].zero_()
            self.value_conv_cache[layer_idx].zero_()



class GearTextScaledWordEmbedding(nn.Embedding):
    """
    This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
    """

    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
        super().__init__(num_embeddings, embedding_dim, padding_idx)
        self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)

    def forward(self, input_ids: torch.Tensor):
        return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)


class GearRMSNorm(nn.Module):
    def __init__(self, dim: int, eps: float = 1e-6):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.zeros(dim))

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

    def forward(self, x):
        output = self._norm(x.float())
        output = output * (1.0 + self.weight.float())
        
        return output.type_as(x)

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


class GearRMSNormGated(nn.Module):
    def __init__(self, config, hidden_size, eps=1e-6, **kwargs):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.eps = eps
        self.act_fn = ACT2FN[config.hidden_activation]

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
    
    def forward(self, x, gate=None):
        # Norm before gate
        output = self._norm(x.float())
        output = output * (1.0 + self.weight.float())
        output = output * self.act_fn(gate.float())
        
        return output.type_as(x)
        

class GearMLP(nn.Module):
    def __init__(self, config: GearConfig):
        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_activation]

    def forward(self, x):
        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
        return down_proj

        
class GearRotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: GearConfig, device=None):
        super().__init__()
        # BC: "rope_type" was originally "type"
        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  # power user: used with advanced RoPE types (e.g. dynamic rope)
    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):  # Force float32
            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)


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, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    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],
    dropout: float = 0.0,
    scaling: Optional[float] = None,
    softcap: Optional[float] = None,
    **kwargs,
) -> tuple[torch.Tensor, torch.Tensor]:
    if scaling is None:
        scaling = module.head_dim**-0.5

    key_states = repeat_kv(key, module.num_key_value_groups)
    value_states = repeat_kv(value, module.num_key_value_groups)

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

    if softcap is not None:
        attn_weights = attn_weights / softcap
        attn_weights = torch.tanh(attn_weights)
        attn_weights = attn_weights * softcap
    if attention_mask is not None:  # no matter the length, we just slice it
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_mask

    # upcast attention to fp32
    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 GearAttention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config: GearConfig, layer_idx: int):
        super().__init__()
        self.is_sliding = config.layer_types[layer_idx] == "sliding_attention"
        self.config = config
        self.layer_idx = layer_idx
        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 = config.query_pre_attn_scalar**-0.5
        self.attention_dropout = self.config.attention_dropout
        self.is_causal = not self.config.use_bidirectional_attention

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
        )
        self.o_proj = nn.Linear(
            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
        )
        self.attn_logit_softcapping = self.config.attn_logit_softcapping
        self.sliding_window = config.sliding_window if self.is_sliding else None

        self.q_norm = GearRMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)
        self.k_norm = GearRMSNorm(dim=config.head_dim, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: torch.Tensor,
        attention_mask: Optional[torch.Tensor],
        past_key_values: Optional[Cache] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)

        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)

        query_states = self.q_norm(query_states)
        key_states = self.k_norm(key_states)

        cos, sin = position_embeddings
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)

        if past_key_values is not None:
            # sin and cos are specific to RoPE models; cache_position needed for the static cache
            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)

        attention_interface: Callable = eager_attention_forward
        if self.config._attn_implementation != "eager":
            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=self.attention_dropout if self.training else 0.0,
            scaling=self.scaling,
            sliding_window=self.sliding_window,
            **kwargs,
        )

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


class GearConvKVGatedMixer(nn.Module):
    """Convolutional Key-Value Gated Mixer is using Key-Value Convolution State and Sigmoid Gating"""
    
    def __init__(
        self,
        config: GearConfig,
        layer_idx: int,
    ):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size
        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.L_cache = config.conv_L_cache
        self.bias = config.attention_bias
        
        # self.adaptive_scaling = nn.Parameter(torch.tensor(1.0))

        self.key_conv = nn.Conv1d(
            in_channels=config.num_key_value_heads * self.head_dim,
            out_channels=config.num_key_value_heads * self.head_dim,
            kernel_size=self.L_cache,
            groups=config.num_key_value_heads * self.head_dim,
            bias=self.bias,
            padding=self.L_cache - 1,
        )
        self.value_conv = nn.Conv1d(
            in_channels=config.num_key_value_heads * self.head_dim,
            out_channels=config.num_key_value_heads * self.head_dim,
            kernel_size=self.L_cache,
            groups=config.num_key_value_heads * self.head_dim,
            bias=self.bias,
            padding=self.L_cache - 1,
        )

        self.q_proj = nn.Linear(
            config.hidden_size, config.num_attention_heads * self.head_dim, bias=self.bias
        )
        self.k_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=self.bias
        )
        self.v_proj = nn.Linear(
            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=self.bias
        )
        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=self.bias)

        self.q_norm = GearRMSNorm(dim=self.head_dim, eps=config.rms_norm_eps)
        self.k_norm = GearRMSNorm(dim=self.head_dim, eps=config.rms_norm_eps)
        
    def apply_mask_to_padding_states(self, hidden_states, attention_mask):
        """
        Tunes out the hidden states for padding tokens, see https://github.com/state-spaces/mamba/issues/66
        """
        # NOTE: attention mask is a 2D boolean tensor
        if hidden_states.shape[1] == 1:
            return hidden_states
        
        if attention_mask is not None and attention_mask.dim() == 2 and attention_mask.shape[1] > 1 and attention_mask.shape[0] > 1:
            dtype = hidden_states.dtype
            hidden_states = (hidden_states * attention_mask[:, :, None]).to(dtype)

        return hidden_states

    def expand_kv(self, kv_states):
        # kv_states: [B, S, kv_heads * head_dim]
        B, S, _ = kv_states.shape
        kv_states = kv_states.view(B, S, self.config.num_key_value_heads, self.head_dim)
        kv_states = kv_states[:, :, :, None, :].expand(
            B,
            S,
            self.config.num_key_value_heads,
            self.num_key_value_groups,
            self.head_dim,
        )
        return kv_states.reshape(B, S, self.config.num_attention_heads * self.head_dim)
    
    def cuda_kernels_forward(
        self,
        hidden_states: torch.Tensor,
        past_key_values: GearHybridKVConvCache | None = None,
        cache_position: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
    ):        
        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)
        hidden_states = self.apply_mask_to_padding_states(hidden_states, attention_mask)
        
        query = self.q_proj(hidden_states).view(hidden_shape)
        key = self.k_proj(hidden_states).view(hidden_shape)
        value = self.v_proj(hidden_states).transpose(-1, -2)

        query = self.q_norm(query).reshape(*input_shape, -1)
        key = self.k_norm(key).reshape(*input_shape, -1).transpose(-1, -2)
                        
        key_conv_weights = self.key_conv.weight.view(self.key_conv.weight.size(0), self.key_conv.weight.size(2))
        value_conv_weights = self.value_conv.weight.view(self.value_conv.weight.size(0), self.value_conv.weight.size(2))
        
        if past_key_values is not None and cache_position[0] > 0:
            key = causal_conv1d_update(
                key.squeeze(-1),
                past_key_values.key_conv_cache[self.layer_idx],
                key_conv_weights,
                self.key_conv.bias,
                None,
            )
            value = causal_conv1d_update(
                value.squeeze(-1),
                past_key_values.value_conv_cache[self.layer_idx],
                value_conv_weights,
                self.value_conv.bias,
                None,
            )
            
            key, value = key.unsqueeze(-1), value.unsqueeze(-1)
        else:
            if past_key_values is not None:
                key_conv_state = nn.functional.pad(key, (self.L_cache - key.shape[-1], 0))
                past_key_values.key_conv_cache[self.layer_idx].copy_(key_conv_state)
                value_conv_state = nn.functional.pad(value, (self.L_cache - value.shape[-1], 0))
                past_key_values.value_conv_cache[self.layer_idx].copy_(value_conv_state)
                
            key = causal_conv1d_fn(key, key_conv_weights, self.key_conv.bias, activation=None)
            value = causal_conv1d_fn(value, value_conv_weights, self.value_conv.bias, activation=None)

        key = self.expand_kv(key.transpose(-1, -2))
        value = self.expand_kv(value.transpose(-1, -2))

        mixer_weights = torch.sigmoid(query * key)
        core_mixer_out = mixer_weights * value
        
        core_mixer_out = self.o_proj(core_mixer_out)
        return core_mixer_out
        
    def torch_native_forward(
        self,
        hidden_states: torch.Tensor,
        past_key_values: GearHybridKVConvCache | None = None,
        cache_position: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
    ):
        seqlen = hidden_states.shape[1]

        input_shape = hidden_states.shape[:-1]
        hidden_shape = (*input_shape, -1, self.head_dim)
        hidden_states = self.apply_mask_to_padding_states(hidden_states, attention_mask)
        
        query = self.q_proj(hidden_states).view(hidden_shape)
        key = self.k_proj(hidden_states).view(hidden_shape)
        value = self.v_proj(hidden_states).transpose(-1, -2)

        query = self.q_norm(query).reshape(*input_shape, -1)
        key = self.k_norm(key).reshape(*input_shape, -1).transpose(-1, -2)

        # NOTE: This native path is kept numerically close to the
        # causal_conv1d fast path:
        #   - the new token always goes to the *last* cache slot (matching
        #     causal_conv1d_update semantics) instead of the previous
        #     clamped-cache_position behavior that misplaced the first
        #     decode steps.
        #   - depthwise reductions are accumulated in fp32 then cast back,
        #     matching the CUDA kernel which also accumulates in fp32.
        if past_key_values is not None and cache_position[0] > 0:
            key_conv_state = past_key_values.key_conv_cache[self.layer_idx]
            key_conv_state = key_conv_state.roll(shifts=-1, dims=-1)
            key_conv_state[:, :, -1] = key.squeeze(-1).to(
                device=key_conv_state.device, dtype=key_conv_state.dtype
            )
            past_key_values.key_conv_cache[self.layer_idx].copy_(key_conv_state)
            key_fp32 = (
                key_conv_state.to(key.device, dtype=torch.float32)
                * self.key_conv.weight[:, 0, :].to(torch.float32)
            ).sum(dim=-1)
            key = key_fp32.to(self.k_proj.weight.dtype)

            value_conv_state = past_key_values.value_conv_cache[self.layer_idx]
            value_conv_state = value_conv_state.roll(shifts=-1, dims=-1)
            value_conv_state[:, :, -1] = value.squeeze(-1).to(
                device=value_conv_state.device, dtype=value_conv_state.dtype
            )
            past_key_values.value_conv_cache[self.layer_idx].copy_(value_conv_state)
            value_fp32 = (
                value_conv_state.to(value.device, dtype=torch.float32)
                * self.value_conv.weight[:, 0, :].to(torch.float32)
            ).sum(dim=-1)
            value = value_fp32.to(self.v_proj.weight.dtype)

            if self.bias and self.key_conv.bias is not None:
                key = key + self.key_conv.bias
            if self.bias and self.value_conv.bias is not None:
                value = value + self.value_conv.bias

            key, value = key.unsqueeze(-1), value.unsqueeze(-1)
        else:
            if past_key_values is not None:
                key_conv_state = nn.functional.pad(key, (self.L_cache - key.shape[-1], 0))
                past_key_values.key_conv_cache[self.layer_idx].copy_(key_conv_state)
                value_conv_state = nn.functional.pad(value, (self.L_cache - value.shape[-1], 0))
                past_key_values.value_conv_cache[self.layer_idx].copy_(value_conv_state)

            # Run conv in fp32 to match the CUDA kernel's accumulation
            # precision; cast result back to the parameter dtype.
            orig_dtype = self.k_proj.weight.dtype
            key_fp = nn.functional.conv1d(
                key.to(torch.float32),
                self.key_conv.weight.to(torch.float32),
                bias=(self.key_conv.bias.to(torch.float32)
                      if self.key_conv.bias is not None else None),
                stride=self.key_conv.stride,
                padding=self.key_conv.padding,
                dilation=self.key_conv.dilation,
                groups=self.key_conv.groups,
            )
            value_fp = nn.functional.conv1d(
                value.to(torch.float32),
                self.value_conv.weight.to(torch.float32),
                bias=(self.value_conv.bias.to(torch.float32)
                      if self.value_conv.bias is not None else None),
                stride=self.value_conv.stride,
                padding=self.value_conv.padding,
                dilation=self.value_conv.dilation,
                groups=self.value_conv.groups,
            )
            key = key_fp[..., :seqlen].to(orig_dtype)
            value = value_fp[..., :seqlen].to(orig_dtype)
        
        key = self.expand_kv(key.transpose(-1, -2))
        value = self.expand_kv(value.transpose(-1, -2))

        mixer_weights = torch.sigmoid(query * key)
        core_mixer_out = mixer_weights * value
        
        core_mixer_out = self.o_proj(core_mixer_out)
        return core_mixer_out

    def forward(
        self,
        hidden_states: torch.Tensor,
        past_key_values: Cache | None = None,
        cache_position: torch.LongTensor = None,
        attention_mask: torch.Tensor | None = None,
    ):
        if is_fast_path_available:
            return self.cuda_kernels_forward(hidden_states, past_key_values, cache_position, attention_mask)
        return self.torch_native_forward(hidden_states, past_key_values, cache_position, attention_mask)

    
class GearDecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: GearConfig, layer_idx: int):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.layer_idx = layer_idx
        self.attention_type = config.layer_types[layer_idx]
        if self.attention_type == "conv_mixer":
            self.local_mixer = GearConvKVGatedMixer(config=config, layer_idx=layer_idx)
        else:
            self.self_attn = GearAttention(config=config, layer_idx=layer_idx)
        self.mlp = GearMLP(config)
        self.input_layernorm = GearRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.post_attention_layernorm = GearRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.pre_feedforward_layernorm = GearRMSNorm(self.hidden_size, eps=config.rms_norm_eps)
        self.post_feedforward_layernorm = GearRMSNorm(self.hidden_size, eps=config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        position_embeddings_global: torch.Tensor,
        position_embeddings_local: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs,
    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        if self.attention_type == "conv_mixer":
            hidden_states = self.local_mixer(
                hidden_states=hidden_states,
                past_key_values=past_key_values,
                cache_position=cache_position,
                attention_mask=attention_mask,
            )
        else:
            # apply global RoPE to non-sliding layer only
            if self.self_attn.is_sliding:
                position_embeddings = position_embeddings_local
            else:
                position_embeddings = position_embeddings_global
                
            hidden_states, self_attn_weights = self.self_attn(
                hidden_states=hidden_states,
                position_embeddings=position_embeddings,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_values=past_key_values,
                output_attentions=output_attentions,
                use_cache=use_cache,
                cache_position=cache_position,
                **kwargs,
            )
        hidden_states = self.post_attention_layernorm(hidden_states)
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.pre_feedforward_layernorm(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = self.post_feedforward_layernorm(hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        return outputs

    
@auto_docstring
class GearPreTrainedModel(PreTrainedModel):
    config: GearConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["GearDecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn = True
    _supports_sdpa = True
    _supports_flex_attn = True

    _can_compile_fullgraph = True
    _supports_attention_backend = True
    _can_record_outputs = {
        "hidden_states": GearDecoderLayer,
        "attentions": GearAttention,
        "conv_mixer": GearConvKVGatedMixer,
    }


@auto_docstring
class GearModel(GearPreTrainedModel):
    config: GearConfig

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

        self.embed_tokens = GearTextScaledWordEmbedding(
            config.vocab_size, config.hidden_size, self.padding_idx, embed_scale=self.config.hidden_size**0.5
        )
        self.layers = nn.ModuleList(
            [GearDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = GearRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = GearRotaryEmbedding(config=config)
        self.gradient_checkpointing = False

        config = copy.deepcopy(config)
        config.rope_theta = config.rope_local_base_freq
        config.rope_scaling = {"rope_type": "default"}
        self.rotary_emb_local = GearRotaryEmbedding(config=config)

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

    # @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,
        past_key_values: Optional[Cache] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> 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

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

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

        # if use_cache and past_key_values is None and not self.training:
        #     batch_size = inputs_embeds.shape[0]
        #     past_key_values = GearHybridKVConvCache(config=self.config, max_batch_size=batch_size, dtype=self.dtype, device=self.device)

        if use_cache and not self.training:
            if past_key_values is None or type(past_key_values).__name__ == "DynamicCache":
                batch_size = inputs_embeds.shape[0]
                past_key_values = GearHybridKVConvCache(
                    config=self.config, 
                    max_batch_size=batch_size, 
                    dtype=self.dtype, 
                    device=self.device
                )

        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)

        # It may already have been prepared by e.g. `generate`
        if not isinstance(causal_mask_mapping := attention_mask, dict):
            # Prepare mask arguments
            mask_kwargs = {
                "config": self.config,
                "input_embeds": inputs_embeds,
                "attention_mask": attention_mask,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "position_ids": position_ids,
            }
            sliding_mask_kwargs = mask_kwargs.copy()

            if self.config.use_bidirectional_attention:
                mask_kwargs["or_mask_function"] = lambda *args: torch.tensor(True, dtype=torch.bool)
                sliding_mask_kwargs["or_mask_function"] = _bidirectional_window_overlay(self.config.sliding_window)

            # Create the masks
            causal_mask_mapping = {
                "full_attention": create_causal_mask(**mask_kwargs),
                "sliding_attention": create_sliding_window_causal_mask(**sliding_mask_kwargs),
                "conv_mixer": attention_mask if inputs_embeds.shape[-1] != 1 else None,
            }

        # embed positions
        hidden_states = inputs_embeds

        # create position embeddings to be shared across the decoder layers
        position_embeddings_global = self.rotary_emb(hidden_states, position_ids)
        position_embeddings_local = self.rotary_emb_local(hidden_states, position_ids)

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

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

            layer_outputs = decoder_layer(
                hidden_states,
                position_embeddings_global=position_embeddings_global,
                position_embeddings_local=position_embeddings_local,
                attention_mask=causal_mask_mapping[decoder_layer.attention_type],
                position_ids=position_ids,
                past_key_values=past_key_values,
                output_attentions=output_attentions,
                use_cache=use_cache,
                cache_position=cache_position,
                **kwargs,
            )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


@auto_docstring
class GearForCausalLM(GearPreTrainedModel, GenerationMixin):
    _tied_weights_keys = ["lm_head.weight"]
    _tp_plan = {"lm_head": "colwise_rep"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
    config: GearConfig

    def __init__(self, config: GearConfig):
        super().__init__(config)
        self.model = GearModel(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()

    @can_return_tuple
    @auto_docstring
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[Cache] = 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,
        cache_position: Optional[torch.LongTensor] = None,
        logits_to_keep: Union[int, torch.Tensor] = 0,
        **kwargs,
    ) -> CausalLMOutputWithPast:

        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
        )
        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs: BaseModelOutputWithPast = 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,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
        logits = self.lm_head(hidden_states[:, slice_indices, :])
        # if self.config.final_logit_softcapping is not None:
        #     logits = logits / self.config.final_logit_softcapping
        #     logits = torch.tanh(logits)
        #     logits = logits * self.config.final_logit_softcapping

        loss = None
        if labels is not None:
            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

    
class GearForSequenceClassification(GenericForSequenceClassification, GearPreTrainedModel):
    pass


class GearForTokenClassification(GenericForTokenClassification, GearPreTrainedModel):
    pass


class GearForQuestionAnswering(GenericForQuestionAnswering, GearPreTrainedModel):
    base_model_prefix = "transformer"  # For BC, where `transformer` was used instead of `model`


__all__ = [
    "GearForCausalLM",
    "GearForQuestionAnswering",
    "GearPreTrainedModel",
    "GearModel",
    "GearForSequenceClassification",
    "GearForTokenClassification",
]