modeling_deepseek_v32.py

# Copyright 2026 the HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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import math
from collections.abc import Callable
from typing import Optional

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

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
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_layers import 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, can_return_tuple, logging
from transformers.utils.generic import check_model_inputs
from .configuration_deepseek_v32 import DeepseekV32Config


logger = logging.get_logger(__name__)


class DeepseekV32RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
        """
        DeepseekV32RMSNorm 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}"


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 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_interleave(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
    r"""
    TODO let's just use the original freqcis computation to not have the view
    transpose + reshape! This is not optimized!
    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.
        position_ids (`torch.Tensor`):
            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
            used to pass offsetted position ids when working with a KV-cache.
        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)

    b, h, s, d = q.shape
    q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)

    b, h, s, d = k.shape
    k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)

    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


def yarn_get_mscale(scale=1, mscale=1):
    if scale <= 1:
        return 1.0
    return 0.1 * mscale * math.log(scale) + 1.0


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: torch.Tensor | None,
    scaling: float,
    dropout: float = 0.0,
    **kwargs: Unpack[TransformersKwargs],
):
    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 attention_mask is not None:
        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
        attn_weights = attn_weights + causal_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 DeepseekV32Indexer(nn.Module):
    def __init__(self, config: "DeepseekV32Config", index_layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = index_layer_idx

        self.hidden_size: int = config.hidden_size
        self.num_heads: int = config.index_n_heads
        self.num_local_heads: int = config.index_n_heads  # world_size handling can be added as needed
        self.head_dim: int = config.index_head_dim
        self.qk_rope_head_dim: int = config.qk_rope_head_dim
        self.index_topk: int = config.index_topk
        self.q_lora_rank: int = config.q_lora_rank

        self.wq_b = nn.Linear(self.q_lora_rank, self.num_heads * self.head_dim, bias=False)
        self.wk = nn.Linear(self.hidden_size, self.head_dim, bias=False)
        self.k_norm = nn.LayerNorm(self.head_dim)
        self.weights_proj = nn.Linear(self.hidden_size, self.num_heads, dtype=torch.get_default_dtype(), bias=False)
        self.softmax_scale = self.head_dim**-0.5

    @torch.no_grad()
    def forward(
        self,
        hidden_states: torch.Tensor,  # [B, S, hidden]
        q_resid: torch.Tensor,  # [B, S, q_lora_rank]
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: torch.Tensor | None,
        past_key_values_index: "Cache",
        cache_position: torch.LongTensor | None,
    ) -> torch.LongTensor:
        B, S, _ = hidden_states.shape
        cos, sin = position_embeddings

        # Queries
        q_states = self.wq_bj(q_resid)  # [B, S, H*D]
        q_states = q_states.view(B, S, self.num_heads, self.head_dim)  # [B, S, H, D]
        q_rot, q_pass = torch.split(q_states, [self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim], dim=-1)
        q_rot = apply_rotary_pos_emb_interleave(q_rot, cos, sin)  # [B, S, H, rope_D]
        q_states = torch.cat([q_rot, q_pass], dim=-1)  # [B, S, H, D]

        # Keys
        k = self.k_norm(self.wk(hidden_states))  # [B, S, D]
        k_rot, k_pass = torch.split(k, [self.qk_rope_head_dim, self.head_dim - self.qk_rope_head_dim], dim=-1)
        # MLA uses single-head rope stream, then expands later; keep [B, 1, S, rope_D] here
        k_rot = k_rot.unsqueeze(1)  # [B, 1, S, rope_D]
        k_rot = apply_rotary_pos_emb_interleave(k_rot, cos, sin)  # [B, 1, S, rope_D]
        k_states = torch.cat(
            [
                k_rot.expand(B, self.num_heads, S, -1),  # expand rope
                k_pass.view(B, 1, S, -1).expand(B, self.num_heads, S, -1),
            ],
            dim=-1,
        )  # [B, H, S, D]

        # Quantize (per provided utilities)
        # Update indexer cache (layer idx belongs to the attention layer using this indexer)
        # We store as: keys = k_fp8 (as [B, 1, S, D] or [B, H, S, D]? We keep [B, 1, S, D] like original)
        # For compactness, collapse heads to 1 for the indexer (you can keep H if your fp8_index expects it).
        k_1h = k_states.mean(dim=1, keepdim=True)  # [B, 1, S, D]  (cheap head merge; adjust if needed)
        k_cache = past_key_values_index.update(k_1h, self.layer_idx, cache_kwargs={"cache_position": cache_position})

        # Weights per head
        head_weights = self.weights_proj(hidden_states) * (self.num_heads**-0.5)  # [B, S, H]
        head_weights = head_weights.unsqueeze(-1) * self.softmax_scale  # [B, S, H, *]
        logits = torch.matmul(k_cache.unsqueeze(1), q_states.transpose(-1, -2))  # [B, M, N, H]

        # ReLU and sum over heads -> [B, M, N]
        logits.clamp_min_(0)
        index_scores = logits.sum(dim=-1)  # [B, M, N]

        if attention_mask is not None:
            index_scores = index_scores + attention_mask

        T = index_scores.shape[-1]
        topk = min(self.index_topk, T)
        topk_indices = index_scores.topk(topk, dim=-1).indices  # [..., topk]
        return topk_indices

class DeepseekV32Attention(nn.Module):
    """
    DeepSeek V3.2 sparse attention mechanism with indexer.

    This implements the native sparse attention from [DeepSeek V3.2](https://huggingface.co/deepseek-ai/DeepSeek-V3.2) which uses
    an indexer to select top-k tokens for attention computation, making it more efficient for long sequences.
    """

    def __init__(self, config: DeepseekV32Config, layer_idx: int):
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.attention_dropout = config.attention_dropout
        self.num_heads = config.num_attention_heads

        self.q_lora_rank = config.q_lora_rank
        self.qk_rope_head_dim = config.qk_rope_head_dim
        self.kv_lora_rank = config.kv_lora_rank
        self.v_head_dim = config.v_head_dim
        self.qk_nope_head_dim = config.qk_nope_head_dim
        self.qk_head_dim = config.qk_head_dim
        self.index_topk = config.index_topk

        self.is_causal = True

        # Query projection
        if self.q_lora_rank is None:
            self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
        else:
            self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias)
            self.q_a_layernorm = DeepseekV32RMSNorm(config.q_lora_rank)
            self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)

        # Key-Value projections
        self.kv_a_proj_with_mqa = nn.Linear(
            config.hidden_size,
            self.kv_lora_rank + self.qk_rope_head_dim,
            bias=config.attention_bias,
        )
        self.kv_a_layernorm = DeepseekV32RMSNorm(self.kv_lora_rank)
        self.kv_b_proj = nn.Linear(
            self.kv_lora_rank,
            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
            bias=False,
        )

        # Output projection
        self.o_proj = nn.Linear(
            self.num_heads * self.v_head_dim,
            config.hidden_size,
            bias=config.attention_bias,
        )

        # Indexer components for sparse attention
        self.wq_b = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)
        self.wk = nn.Linear(config.hidden_size, self.qk_head_dim, bias=config.attention_bias)
        self.k_norm = DeepseekV32RMSNorm(self.qk_head_dim)
        self.weights_proj = nn.Linear(config.hidden_size, self.num_heads, bias=False)

        self.scaling = self.qk_head_dim ** (-0.5)
        if self.config.rope_scaling.get("rope_type", "default") != "default":
            mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
            scaling_factor = self.config.rope_scaling["factor"]
            if mscale_all_dim:
                mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
                self.scaling = self.scaling * mscale * mscale

        self.indexer = DeepseekV32Indexer(config, layer_idx)

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

        # For training or when index_topk is not effective, fall back to standard attention
        # This is a simplified implementation - in practice, you'd implement the full sparse indexer
        if self.training or seq_length <= self.index_topk:
            logger.warning_once(
                    "DeepSeek V3.2 sparse attention is not fully implemented in this version. "
                    "Falling back to standard attention. For production use, please use vLLM or "
                    "other optimized inference engines.",
            )
            return self._standard_attention(
                hidden_states, position_embeddings, attention_mask, past_key_values, cache_position, **kwargs
            )

        # Sparse attention implementation would go here
        # This requires custom CUDA kernels for efficient top-k selection and indexing
        return self._dsa_attention(
            hidden_states, position_embeddings, attention_mask, past_key_values, cache_position, **kwargs
        )

    def _standard_attention(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: torch.Tensor | None,
        past_key_values: Cache | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
        """Standard attention fallback (same as DeepSeek V3)"""
        batch_size, seq_length = hidden_states.shape[:-1]
        query_shape = (batch_size, seq_length, -1, self.qk_head_dim)
        key_shape = (batch_size, seq_length, -1, self.qk_nope_head_dim + self.v_head_dim)

        if self.q_lora_rank is None:
            q_states = self.q_proj(hidden_states)
        else:
            q_states = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
        q_states = q_states.view(query_shape).transpose(1, 2)
        q_pass, q_rot = torch.split(q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)

        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
        k_pass, k_rot = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)

        k_pass = self.kv_b_proj(self.kv_a_layernorm(k_pass)).view(key_shape).transpose(1, 2)
        k_pass, value_states = torch.split(k_pass, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)

        k_rot = k_rot.view(batch_size, 1, seq_length, self.qk_rope_head_dim)

        cos, sin = position_embeddings
        if self.config.rope_interleave:
            q_rot, k_rot = apply_rotary_pos_emb_interleave(q_rot, k_rot, cos, sin)
        else:
            q_rot, k_rot = apply_rotary_pos_emb(q_rot, k_rot, cos, sin)
        k_rot = k_rot.expand(*k_pass.shape[:-1], -1)

        query_states = torch.cat((q_pass, q_rot), dim=-1)
        key_states = torch.cat((k_pass, k_rot), dim=-1)

        if past_key_values is not None:
            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)

        if self.config._attn_implementation == "flash_attention_2" and self.qk_head_dim != self.v_head_dim:
            value_states = F.pad(value_states, [0, self.qk_head_dim - self.v_head_dim])

        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
            self.config._attn_implementation, eager_attention_forward
        )

        attn_output, attn_weights = attention_interface(
            self,
            query_states,
            key_states,
            value_states,
            attention_mask,
            dropout=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )

        if self.config._attn_implementation == "flash_attention_2" and self.qk_head_dim != self.v_head_dim:
            attn_output = attn_output[:, :, :, : self.v_head_dim]

        attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous()
        attn_output = self.o_proj(attn_output)
        return attn_output, attn_weights

    def _dsa_attention(
        self,
        hidden_states: torch.Tensor,
        position_embeddings: tuple[torch.Tensor, torch.Tensor],
        attention_mask: torch.Tensor | None,
        past_key_values: Cache | None = None,
        cache_position: torch.LongTensor | None = None,
        **kwargs: Unpack[FlashAttentionKwargs]
    ):

        B, S, _ = hidden_states.shape
        cos, sin = position_embeddings

        # ----- Q path -----
        q_resid = self.q_a_layernorm(self.q_a_proj(hidden_states))  # [B, S, q_lora_rank]
        q_states = self.q_b_proj(q_resid).view(B, S, self.num_heads, self.qk_head_dim)  # [B, S, H, D]
        # Split into pass/rot then apply RoPE on q_rot
        q_pass, q_rot = torch.split(q_states, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
        q_rot = apply_rotary_pos_emb(q_rot, cos, sin)  # [B, S, H, rope_D]
        q_states = torch.cat([q_pass, q_rot], dim=-1)  # [B, S, H, D]

        # Layout for matmul: [B, H, S, D]
        q_states = q_states.transpose(1, 2).contiguous()  # [B, H, S, D]

        # ----- KV path (compressed + rope stream) -----
        kv_all = self.kv_a_proj_with_mqa(hidden_states)  # [B, S, kv_rank + rope_D]
        kv_compressed, k_rot = torch.split(kv_all, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
        kv_compressed = self.kv_a_layernorm(kv_compressed)  # [B, S, kv_rank]
        # Pre-project to K_pass and V
        kv_proj = self.kv_b_proj(kv_compressed)  # [B, S, H*(qk_nope + v)]
        kv_proj = kv_proj.view(B, S, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
        k_pass, v_states = torch.split(
            kv_proj, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
        )  # [B,S,H,nope], [B,S,H,V]

        # Rope on K side: keep a single-head rope stream like MLA, then expand
        k_rot = k_rot.view(B, 1, S, self.qk_rope_head_dim)  # [B, 1, S, rope_D]
        k_rot = apply_rotary_pos_emb(k_rot, cos, sin)  # [B, 1, S, rope_D]

        # Concatenate K = [K_pass, K_rot(expanded)]
        k_states = torch.cat(
            (
                k_pass.transpose(1, 2),  # [B, H, S, nope_D]
                k_rot.expand(B, self.num_heads, S, -1),
            ),  # [B, H, S, rope_D]
            dim=-1,
        )  # [B, H, S, D]
        v_states = v_states.transpose(1, 2).contiguous()  # [B, H, S, V]

        # ----- Cache update/usage -----
        if past_key_values is not None:
            # Store compressed stream & rope stream (as in original MLA path)
            # We cache `kv_compressed` under `keys` and `k_rot` under `values` in MlaLayer.
            # Shapes must be [B, H, t, *] and [B, 1, t, rope_D].
            kv_comp_cache = kv_compressed.view(B, 1, S, self.kv_lora_rank).expand(B, self.num_heads, S, -1)
            k_rot_cache = k_rot  # [B, 1, S, rope_D]
            cached_kv, cached_pe = past_key_values.update(
                kv_comp_cache, k_rot_cache, layer_idx=self.layer_idx, cache_kwargs={"cache_position": cache_position}
            )
            # Decode path makes use of cached projections; Prefill can use full K/V directly.

        # ----- Two paths (prefill vs decode) -----
        if attention_mask is not None:
            # Prefill (full attention over local window): standard scaled dot-product with top-k pruning from indexer

            # Build scores: [B, H, S, S_total]
            # K layout already [B, H, T, D]
            scores = (q_states.float() @ k_states.float().transpose(-1, -2)) * self.scaling  # [B, H, S, T]

            # Indexer top-k
            if past_key_values is not None:
                topk_idx = self.indexer(
                    hidden_states,
                    q_resid,
                    position_embeddings,
                    attention_mask,
                    past_key_values_index=past_key_values,  # we reuse same Cache with IndexerLayer? (separate cache recommended)
                    cache_position=cache_position,
                )
                # Build mask to keep only top-k per (B,S,head?)
                # Expect topk_idx shape to broadcast to [B, H, S, T]. We scatter along last dim.
                keep_mask = torch.full_like(scores, float("-inf"))
                # If topk_idx is [B,S,topk], expand for heads:
                if topk_idx.dim() == 3:
                    topk_idx = topk_idx.unsqueeze(1).expand(B, self.num_heads, S, -1)
                keep_mask.scatter_(-1, topk_idx, 0.0)
                scores = scores + keep_mask

            probs = nn.functional.softmax(scores, dim=-1, dtype=torch.float32).type_as(hidden_states)  # [B, H, S, T]
            attn_output = probs @ v_states  # [B, H, S, V]

        elif past_key_values is not None:
            # Decode: use cached compressed KV & rope stream to recompose attention scores efficiently
            # Compose q_pass and q_rot pieces as in MLA math, but via matmul
            # 1) Rebuild "nope" term via kv_b weights (dequant on the fly)
            wkv_b = self.kv_b_proj.weight.view(
                self.num_heads, self.qk_nope_head_dim + self.v_head_dim, self.kv_lora_rank
            )
            w_k_nope = wkv_b[:, : self.qk_nope_head_dim, :]  # [H, nope_D, kv_rank]
            w_v = wkv_b[:, self.qk_nope_head_dim :, :]  # [H, V,     kv_rank]

            # q_pass: [B,H,S,nope_D]; cached_kv: [B,H,T,kv_rank]
            q_pass = q_states[..., : self.qk_nope_head_dim]  # [B,H,S,nope_D]
            kv_comp = past_key_values[self.layer_idx][0]  # keys -> [B,H,T,kv_rank]
            pe_full = past_key_values[self.layer_idx][1]  # values -> [B,1,T,rope_D]
            # Project q_pass with w_k_nope: [B,H,S,kv_rank]
            qk_nope = torch.matmul(q_pass, w_k_nope.transpose(-1, -2))  # [B,H,S,kv_rank]
            # Scores_nope = qk_nope @ kv_comp^T
            scores_nope = torch.matmul(qk_nope.float(), kv_comp.float().transpose(-1, -2))  # [B,H,S,T]

            # 2) Rope term: q_rot @ k_rot^T
            q_rot_only = q_states[..., -self.qk_rope_head_dim :]  # [B,H,S,rope_D]
            k_rot_only = pe_full.expand(B, self.num_heads, -1, -1)  # [B,H,T,rope_D]
            scores_rot = torch.matmul(q_rot_only.float(), k_rot_only.float().transpose(-1, -2))  # [B,H,S,T]

            scores = (scores_nope + scores_rot) * self.scaling

            # Indexer top-k (decode)
            topk_idx = self.indexer(
                hidden_states,
                q_resid,
                position_embeddings,
                attention_mask,
                past_key_values_index=past_key_values,
                cache_position=cache_position,
            )
            # For decode single-step S==1 typically; build a [B,H,1,T] mask
            keep_mask = torch.full_like(scores, float("-inf"))
            if topk_idx.dim() == 3:
                topk_idx = topk_idx.unsqueeze(1).expand(B, self.num_heads, S, -1)
            keep_mask.scatter_(-1, topk_idx, 0.0)
            scores = scores + keep_mask

            probs = nn.functional.softmax(scores, dim=-1, dtype=torch.float32).type_as(hidden_states)  # [B,H,S,T]

            # Rebuild V for decode fast-path: v = (kv_comp @ w_v^T)
            # kv_comp: [B,H,T,kv_rank], w_v: [H, V, kv_rank]
            v_from_comp = torch.matmul(kv_comp, w_v.transpose(-1, -2))  # [B,H,T,V]
            attn_output = torch.matmul(probs, v_from_comp)  # [B,H,S,V]

        # Output projection
        attn_output = attn_output.transpose(1, 2).reshape(B, S, -1).contiguous()  # [B,S,H*V]
        attn_output = self.o_proj(attn_output)  # [B,S,hidden]
        return attn_output, None



class DeepseekV32MLP(nn.Module):
    def __init__(self, config, intermediate_size=None):
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size if intermediate_size is None else 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


class DeepseekV32TopkRouter(nn.Module):
    def __init__(self, config: DeepseekV32Config):
        super().__init__()
        self.config = config
        self.top_k = config.num_experts_per_tok
        self.n_routed_experts = config.n_routed_experts
        self.routed_scaling_factor = config.routed_scaling_factor
        self.n_group = config.n_group
        self.topk_group = config.topk_group
        self.norm_topk_prob = config.norm_topk_prob

        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, config.hidden_size)))
        self.register_buffer("e_score_correction_bias", torch.zeros((self.n_routed_experts), dtype=torch.float32))

    def forward(self, hidden_states):
        hidden_states = hidden_states.view(-1, self.config.hidden_size)
        router_logits = F.linear(hidden_states.type(torch.float32), self.weight.type(torch.float32))
        return router_logits


class DeepseekV32MoE(nn.Module):
    """
    A mixed expert module containing shared experts.
    """

    def __init__(self, config):
        super().__init__()
        self.config = config
        self.experts = nn.ModuleList(
            [
                DeepseekV32MLP(config, intermediate_size=config.moe_intermediate_size)
                for _ in range(config.n_routed_experts)
            ]
        )
        self.gate = DeepseekV32TopkRouter(config)
        self.shared_experts = DeepseekV32MLP(
            config=config, intermediate_size=config.moe_intermediate_size * config.n_shared_experts
        )

    def moe(self, hidden_states: torch.Tensor, topk_indices: torch.Tensor, topk_weights: torch.Tensor):
        r"""
        CALL FOR CONTRIBUTION! I don't have time to optimise this right now, but expert weights need to be fused
        to not have to do a loop here (deepseek has 256 experts soooo yeah).
        """
        final_hidden_states = torch.zeros_like(hidden_states, dtype=topk_weights.dtype)
        expert_mask = torch.nn.functional.one_hot(topk_indices, num_classes=len(self.experts))
        expert_mask = expert_mask.permute(2, 0, 1)

        for expert_idx in range(len(self.experts)):
            expert = self.experts[expert_idx]
            mask = expert_mask[expert_idx]
            token_indices, weight_indices = torch.where(mask)

            if token_indices.numel() > 0:
                expert_weights = topk_weights[token_indices, weight_indices]
                expert_input = hidden_states[token_indices]
                expert_output = expert(expert_input)
                weighted_output = expert_output * expert_weights.unsqueeze(-1)
                final_hidden_states.index_add_(0, token_indices, weighted_output)

        # in original deepseek, the output of the experts are gathered once we leave this module
        # thus the moe module is itelsf an IsolatedParallel module
        # and all expert are "local" meaning we shard but we don't gather
        return final_hidden_states.type(hidden_states.dtype)

    def forward(self, hidden_states):
        residuals = hidden_states
        orig_shape = hidden_states.shape
        topk_indices, topk_weights = self.gate(hidden_states)
        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
        hidden_states = self.moe(hidden_states, topk_indices, topk_weights).view(*orig_shape)
        hidden_states = hidden_states + self.shared_experts(residuals)
        return hidden_states



class DeepseekV32DecoderLayer(GradientCheckpointingLayer):
    def __init__(self, config: DeepseekV32Config, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.self_attn = DeepseekV32Attention(config, layer_idx)

        if layer_idx >= config.first_k_dense_replace:
            self.mlp = DeepseekV32MoE(config)
        else:
            self.mlp = DeepseekV32MLP(config)

        self.input_layernorm = DeepseekV32RMSNorm(config.hidden_size, config.rms_norm_eps)
        self.post_attention_layernorm = DeepseekV32RMSNorm(config.hidden_size, config.rms_norm_eps)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        use_cache: bool | None = False,
        cache_position: torch.LongTensor | None = None,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> torch.Tensor:
        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,
            past_key_values=past_key_values,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **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


class DeepseekV32PreTrainedModel(PreTrainedModel):
    config: DeepseekV32Config
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["DeepseekV32DecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn = True
    _supports_sdpa = True
    _supports_flex_attn = True
    _can_compile_fullgraph = False
    _supports_attention_backend = True
    _can_record_outputs = {
        "hidden_states":DeepseekV32DecoderLayer,
        "attentions": DeepseekV32Attention,
    }

    @torch.no_grad()
    def _init_weights(self, module):
        super()._init_weights(module)
        if isinstance(module, DeepseekV32TopkRouter):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)


class DeepseekV32RotaryEmbedding(nn.Module):
    inv_freq: torch.Tensor  # fix linting for `register_buffer`

    def __init__(self, config: DeepseekV32Config, device=None):
        super().__init__()
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config

        self.rope_type = self.config.rope_scaling.get("rope_type", "default")
        rope_init_fn: Callable = self.compute_default_rope_parameters
        if self.rope_type != "default":
            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)

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

    @staticmethod
    def compute_default_rope_parameters(
        config: DeepseekV32Config | None = None,
        device: Optional["torch.device"] = None,
        seq_len: int | None = None,
    ) -> tuple["torch.Tensor", float]:
        """
        Computes the inverse frequencies according to the original RoPE implementation
        Args:
            config ([`~transformers.PreTrainedConfig`]):
                The model configuration.
            device (`torch.device`):
                The device to use for initialization of the inverse frequencies.
            seq_len (`int`, *optional*):
                The current sequence length. Unused for this type of RoPE.
        Returns:
            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
        """
        base = config.rope_theta
        partial_rotary_factor = config.rope_scaling.get("partial_rotary_factor", 1.0)
        head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
        dim = int(head_dim * partial_rotary_factor)

        attention_factor = 1.0  # Unused in this type of RoPE

        # Compute the inverse frequencies
        inv_freq = 1.0 / (
            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
        )
        return inv_freq, attention_factor

    @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)


class DeepseekV32Model(DeepseekV32PreTrainedModel):
    _keys_to_ignore_on_load_unexpected = [r"model\.layers\.78.*"]

    def __init__(self, config: DeepseekV32Config):
        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(
            [DeepseekV32DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self.norm = DeepseekV32RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = DeepseekV32RotaryEmbedding(config=config)
        self.gradient_checkpointing = False

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

    @check_model_inputs
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        cache_position: torch.LongTensor | None = None,
        use_cache: bool | None = 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: torch.Tensor = self.embed_tokens(input_ids)

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache(config=self.config)

        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.Tensor = (
                torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
            )

        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = create_causal_mask(
            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,
        )

        hidden_states = inputs_embeds
        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)

        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
            hidden_states = decoder_layer(
                hidden_states,
                attention_mask=causal_mask,
                position_embeddings=position_embeddings,
                position_ids=position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                **kwargs,
            )

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


class DeepseekV32ForCausalLM(DeepseekV32PreTrainedModel, GenerationMixin):
    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
    _tp_plan = {"lm_head": "colwise_gather_output"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config):
        super().__init__(config)
        self.model = DeepseekV32Model(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
    def forward(
        self,
        input_ids: torch.LongTensor | None = None,
        attention_mask: torch.Tensor | None = None,
        position_ids: torch.LongTensor | None = None,
        past_key_values: Cache | None = None,
        inputs_embeds: torch.FloatTensor | None = None,
        labels: torch.LongTensor | None = None,
        use_cache: bool | None = None,
        cache_position: torch.LongTensor | None = None,
        logits_to_keep: int | torch.Tensor = 0,
        **kwargs: Unpack[TransformersKwargs],
    ) -> CausalLMOutputWithPast:
        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,
            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, :])

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

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


__all__ = ["DeepseekV32PreTrainedModel", "DeepseekV32Model", "DeepseekV32ForCausalLM"]