modeling_ernie4_5_moe.py

# Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from copy import deepcopy
from dataclasses import dataclass
from functools import partial
from typing import Callable, Optional, Tuple, Union

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

from transformers.cache_utils import Cache, DynamicCache, SlidingWindowCache, StaticCache
from transformers.generation import GenerationMixin
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
from transformers.modeling_outputs import ModelOutput, MoeCausalLMOutputWithPast
from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.processing_utils import Unpack
from transformers.utils import LossKwargs, auto_docstring, can_return_tuple, logging, is_torch_flex_attn_available

from .configuration_ernie4_5_moe import Ernie4_5_MoeConfig


if is_torch_flex_attn_available():
    from torch.nn.attention.flex_attention import BlockMask

    from transformers.integrations.flex_attention import make_flex_block_causal_mask

logger = logging.get_logger(__name__)


class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...

@dataclass
class Erine4_5_MoeModelOutputWithPast(ModelOutput):
    last_hidden_state: Optional[torch.FloatTensor] = None
    past_key_values: Optional[Cache] = None
    hidden_states: Optional[tuple[torch.FloatTensor, ...]] = None
    attentions: Optional[tuple[torch.FloatTensor, ...]] = None
    router_loss: Optional[torch.FloatTensor] = None
    gate_logits: Optional[tuple[torch.FloatTensor, ...]] = None
    mtp_outputs: Optional[torch.FloatTensor] = None


@dataclass
class Ernie4_5_MoeCausalLMOutputWithPast(MoeCausalLMOutputWithPast):
    router_loss: Optional[torch.FloatTensor] = None

def rotate_half(x):
    """Rotates half the hidden dims of the input."""

    x1 = x[..., 0::2]
    x2 = x[..., 1::2]
    return torch.stack((-x2, x1), dim=-1).reshape(x.shape)


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 apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, 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.
        position_ids (`torch.Tensor`, *optional*):
            Deprecated and unused.
        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.
    """
    orig_dtype = q.dtype
    sin_pos = torch.stack([sin, sin], dim=-1).reshape(*sin.shape[:-1],-1)
    cos_pos = torch.stack([cos, cos], dim=-1).reshape(*sin.shape[:-1],-1)
    q_embed = (q.float() * cos_pos) + (rotate_half(q).float() * sin_pos)
    k_embed = (k.float() * cos_pos) + (rotate_half(k).float() * sin_pos)
    return q_embed.to(orig_dtype), k_embed.to(orig_dtype)


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,
):
    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.to(attn_weights.device)

    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


def topk_gate_func(
    module: nn.Module,
    hidden_states: torch.Tensor,
):
    capacity = module.get_capacity(hidden_states.shape[0])
    with torch.autocast(device_type='cuda',dtype=torch.float32):
        logits = module.gate(hidden_states.float())
    router_loss = torch.zeros([1], dtype=torch.float32, device=hidden_states.device)
    router_loss.detach()
    return logits, capacity, router_loss


class Ernie4_5_ResidualWithDropout(nn.Module):
    """
    Fused dropout implementation with residual connection support.

    This layer combines dropout and residual addition in a single operation for better performance,
    particularly on GPU devices. The dropout is conditionally applied based on the probability.

    Args:
        prob (float): Dropout probability (between 0 and 1)

    Attributes:
        prob (float): Stores the dropout probability
        dropout (nn.Dropout): The actual dropout layer instance
    """

    def __init__(self, prob): 
        """
        Initialize the fused dropout layer.

        Args:
            prob (float): Dropout probability (0 means no dropout)
        """
        super().__init__()
        self.prob = prob
        self.dropout = nn.Dropout(p=prob)

    def forward(self, x, y):
        """
        Forward pass of the fused dropout layer.

        Args:
            x (torch.Tensor): Input tensor to potentially apply dropout on
            y (torch.Tensor): Residual tensor to add to the (possibly dropped out) x

        Returns:
            torch.Tensor: Result of x (with optional dropout) + y
        """
        if self.prob > 0:
            x = self.dropout(x)
        output = x + y

        return output


class Ernie4_5_Attention(nn.Module):
    """Multi-headed attention from 'Attention Is All You Need' paper"""

    def __init__(self, config, layer_idx=0):
        """
        Args:
            config (ErnieConfig): Model configuration.
            layer_idx (int, optional): Index in transformer stack. Defaults to 0.
        """
        super().__init__()
        self.layer_idx = layer_idx
        self.hidden_size = config.hidden_size
        self.num_heads = config.num_attention_heads
        self.num_key_value_heads = config.num_key_value_heads if config.num_key_value_heads is not None else self.nums_head
        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
        self.head_dim = self.hidden_size // self.num_heads
        self.freq_allocation = config.freq_allocation if hasattr(config, "freq_allocation") else 0
        self.scaling = self.head_dim**-0.5
        self.attention_dropout = getattr(config, "attention_probs_dropout_prob", 0.0)
        self.is_causal = True

        self.q_proj = nn.Linear(
            self.hidden_size,
            self.num_heads * self.head_dim,
            bias=config.use_bias,
        )

        self.k_proj = nn.Linear(
            self.hidden_size,
            self.num_key_value_heads * self.head_dim,
            bias=config.use_bias,
        )

        self.v_proj = nn.Linear(
            self.hidden_size,
            self.num_key_value_heads * self.head_dim,
            bias=config.use_bias,
        )

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


    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        past_key_value: Optional[Cache] = None,
        position_ids: Optional[torch.Tensor] = None,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: tuple[torch.Tensor, torch.Tensor] = None,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor, torch.Tensor]]]:
        B, L = hidden_states.shape[:-1]

        query_states = self.q_proj(hidden_states).view(B, L, self.num_heads, -1).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(B, L, self.num_key_value_heads, -1).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(B, L, self.num_key_value_heads, -1).transpose(1, 2)

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

        if past_key_value is not None:
            # sin and cos are specific to RoPE models; cache_position needed for the static cache
            cache_kwargs = {"cache_position": cache_position}
            key_states, value_states = past_key_value.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=0.0 if not self.training else self.attention_dropout,
            scaling=self.scaling,
            **kwargs,
        )
        attn_output = attn_output.reshape(B, L, -1).contiguous()
        attn_output = self.o_proj(attn_output)

        return attn_output, attn_weights


class Ernie4_5_MLP(nn.Module):
    """
    Ernie4_5_MLP - Gated Multi-Layer Perceptron module used in Ernie model.
    """

    def __init__(self, config,intermediate_size=None):
        """
        Initialize the MLP module with configuration options.

        Args:
            config: Model configuration object with attributes:
                - hidden_size: int
                - intermediate_size: int
                - use_bias: bool
            layer_idx (int): Index of current layer (default: 0)
        """
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.intermediate_size = intermediate_size if intermediate_size is not None else config.intermediate_size
        self.gate_proj =  nn.Linear(self.hidden_size, self.intermediate_size, bias=config.use_bias)
        self.up_proj =  nn.Linear(self.hidden_size, self.intermediate_size, bias=config.use_bias)
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.use_bias)


    def forward(self, x):
        """
        Args:
            x (Tensor): shape [batch_size, seq_len, hidden_size]

        Returns:
            Tensor: shape [batch_size, seq_len, hidden_size]
        """
        down_proj = self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
        return down_proj


class Ernie4_5_MoeStatics(nn.Module):
    """
    Stores MoE (Mixture of Experts) statistics
    and expert usage information.
    """

    def __init__(self, config):
        """
        Initialize MoE statistics tracking.

        Args:
            config: Model configuration containing MoE parameters
        """
        super().__init__()

        num_experts = config.moe_num_experts
        num_experts_groups = 1

        self.e_score_correction_bias = nn.Parameter(
            torch.zeros(num_experts_groups, num_experts, dtype=torch.float32),
            requires_grad=False
        )

class Ernie4_5_MoeMLP(nn.Module):
    """Mixture of Experts (MoE) variant of ERNIE's MLP layer."""

    def __init__(self,config):
        super().__init__()
        self.config = config
        self.k = config.moe_k
        self.sinkhorn_2gate = config.sinkhorn_2gate
        self.sinkhorn_temp = config.sinkhorn_temp

        moe_intermediate_size = config.moe_intermediate_size if config.moe_intermediate_size else config.intermediate_size
        self.gate = nn.Linear(config.hidden_size, config.moe_num_experts, bias=False, dtype=torch.float32)
        if config.moe_gate_act == "softmax":
            self.gate_act = partial(F.softmax, dim=-1)
        elif config.moe_gate_act == "sigmoid":
            self.gate_act = F.sigmoid
        else:
            raise ValueError(f"{config.moe_gate_act} is not supported.")
        
        self.experts = nn.ModuleList(
            [Ernie4_5_MLP(config,moe_intermediate_size) for i in range(config.moe_num_experts)]
        )
        
        if config.moe_use_aux_free:
            self.moe_statics = Ernie4_5_MoeStatics(config)
        
        self.use_correction_bias = config.moe_use_aux_free
        self.num_local_experts = len(self.experts)

        self.shared_experts = self._init_shared_experts()

    def _init_shared_experts(self):
        """
        Initialize the shared expert module.

        Returns:
            shared_experts: Shared expert module, returns None if no shared experts are needed.

        """
        cfg = deepcopy(self.config)
        if getattr(cfg, 'moe_num_shared_experts', 0) > 0:
            if getattr(cfg, 'moe_intermediate_size', None):
                cfg.intermediate_size = cfg.moe_intermediate_size * cfg.moe_num_shared_experts
            else:
                cfg.intermediate_size = cfg.intermediate_size * cfg.moe_num_shared_experts
            shared_experts = Ernie4_5_MLP(cfg, cfg.intermediate_size)
        else:
            shared_experts = None
        return shared_experts

    def forward(
        self,
        input: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Forward pass through MoE layer.

        Args:
            input (Tensor): Input tensor of shape [s, d].
            token_type_ids: Optional tensor for token types.

        Returns:
            tuple: (output, combine_weights, router_loss, gate_logits)
        """

        if input.dim() == 3:
            orig_shape = input.shape
            input = input.reshape(-1, input.shape[-1])
        else:
            orig_shape = None
        assert input.dim() == 2, f"input Tensor must have dimensions: (s)equence, (d)im, got:{input.shape}"

        assert self.gate is not None

        gate_input = input

        (
            dispatched_input,
            combine_weights,
            dispatch_mask,
            scatter_index,
            router_loss,
            gate_logits,
            gate_prob
        ) = self.gate_and_dispatch(gate_input)

        expert_out = self.forward_experts(dispatched_input)

        combined_output = self.combine_expert_output(expert_out, combine_weights, scatter_index)

        if self.shared_experts is not None:
            shared_expert_out = self.shared_experts(gate_input)
            combined_output += shared_expert_out

        if orig_shape:
            combined_output = combined_output.reshape(orig_shape[:-1] + (combined_output.shape[-1],))

        return combined_output, combine_weights, router_loss, gate_logits

    def forward_experts(self, dispatched_input: torch.Tensor) -> torch.Tensor:
        """
        Forward pass through experts sequentially.

        Args:
            dispatched_input (Tensor): Input tensor of shape [num_experts, capacity, dim].

        Returns:
            Tensor: Expert outputs of shape [num_experts, capacity, dim].
        """
        true_experts = self.experts
        dispatched_input = dispatched_input.reshape(
            1, self.num_local_experts, -1, dispatched_input.shape[-1]
        )
        expert_outputs = []
        if isinstance(self.experts, nn.ModuleList):
            chunks = dispatched_input.permute(1, 0, 2, 3).contiguous().unbind(0)
            assert len(chunks) == len(true_experts), f"{len(chunks)}, {len(true_experts)}"
            for chunk, expert in zip(chunks, true_experts):
                expert_outputs.append(expert(chunk))
        else:
            dispatched_input = dispatched_input.permute(1, 0, 2, 3).contiguous()
            orig_shape = dispatched_input.shape
            chunks = dispatched_input.reshape(orig_shape[0], -1, orig_shape[-1])
            chunks = self.experts(chunks)
            chunks = chunks.reshape(orig_shape[:-1] + (chunks.shape[-1],)).unbind(0)
            expert_outputs.extend(chunks)
            
        expert_output = torch.stack(expert_outputs, dim=1)
        return expert_output

    def moe_gate_dispatch(
        self,
        x: torch.Tensor,                
        gate_logits: torch.Tensor,
        k: int,
        capacity: Optional[int],
    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor,
               torch.Tensor, torch.Tensor]:

        S, H = x.shape
        E = gate_logits.shape[1]
        device = x.device
        topk_prob, topk_idx = torch.topk(gate_logits, k, dim=-1)               
        combine_weights = topk_prob                                     
        expert_id = topk_idx                                            
        y = x.new_zeros((E, capacity, H))                           
        scatter_index = x.new_full((k, S), -1, dtype=torch.int32)
        
        # per-expert slot counters
        slot_counter = torch.zeros(E, dtype=torch.int32, device=device)

        for tok in range(S):
            for route in range(k):
                e = expert_id[tok, route].item()
                slot = slot_counter[e].item()
                if slot >= capacity:
                    combine_weights[tok, route] = 0.0
                    continue

                # record mapping & dispatch activation
                scatter_index[route, tok] = e * capacity + slot
                y[e, slot] = x[tok]
                slot_counter[e] += 1

        expert_offset = torch.cumsum(slot_counter, 0, dtype=torch.int64)

        return y, combine_weights, scatter_index, expert_offset, expert_id
    
    def combine_expert_output(self, expert_output: torch.Tensor, combine_weights: torch.Tensor, scatter_index: torch.Tensor) -> torch.Tensor:
        """
        Combine expert outputs using combination weights.

        Args:
            expert_output (Tensor): Expert outputs [num_experts, capacity, dim].
            combine_weights (Tensor): Combination weights.
            scatter_index (Tensor): Scatter indices.

        Returns:
            Tensor: Combined output [seqlen, dim].
        """
        expert_output = expert_output.reshape(-1, expert_output.shape[-1])
        combined_output = self.combining(expert_output, combine_weights, scatter_index)
        return combined_output

    def combining(self, x, combine_weights, scatter_index):
        """
        Combines and aggregates input matrix using combination weights.

        Args:
            x (Tensor): Input tensor of shape [num_experts * capacity, dim]
            combine_weights (Tensor): Combination weights of shape [seq, 2]
            scatter_index (Tensor): Scatter indices of shape [seq, 2]

        Returns:
            Tensor: Combined output tensor of shape [seq, dim]
        """
        dim = x.shape[-1]

        scatter_index = scatter_index.reshape([-1])
        num_k = combine_weights.shape[-1]
        
        combine_weights = combine_weights.unsqueeze(1)

        x = x[scatter_index].reshape([-1, num_k, dim])

        return torch.matmul(combine_weights, x).squeeze(1)

    def gate_and_dispatch(self, input):
        """
        Calculate gate and dispatch inputs.

        Args:
            input: Input tensor of shape [seq, dim]

        Returns:
            tuple: (dispatched_input, combine_weights, dispatch_mask,
            scatter_index, router_loss, gate_logits, gate_prob)
        """
        gate_logits, capacity, router_loss = topk_gate_func(
            self,
            input,
        )

        # capacity no use
        prob = self.gate_act(gate_logits)
        (
            dispatched_input,
            combine_weights_unnorm,
            scatter_index,
            dispatch_mask,
            _,
        ) = self.moe_gate_dispatch(input, prob,  k=self.k, capacity=capacity)
        dispatch_mask = torch.diff(F.pad(dispatch_mask, (1, 0)))

        scatter_index.detach()
        dispatch_mask.detach()

        scatter_index = scatter_index.transpose(0, 1)  # [k, s] -> [s, k]
        combine_weights = combine_weights_unnorm / torch.clamp(
            combine_weights_unnorm.sum(dim=-1, keepdim=True), min=1e-12
        )
        combine_weights = combine_weights.to(dtype=dispatched_input.dtype)

        return dispatched_input, combine_weights, dispatch_mask, scatter_index, router_loss, gate_logits, prob

    def get_capacity(self, num_tokens, cap_factor=None):
        """
        Calculate capacity based on number of tokens.

        Args:
            num_tokens: Number of input tokens
            cap_factor: Optional capacity factor override

        Returns:
            int: Calculated capacity
        """
        num_experts = self.config.moe_num_experts
        if cap_factor is not None:
            cap = cap_factor
        else:
            if self.training:
                cap = self.config.moe_capacity[0]
            elif num_tokens < num_experts:
                cap = self.config.moe_capacity[2]
            else:
                cap = self.config.moe_capacity[1]

        capacity = int(cap * num_tokens // num_experts)
        assert capacity > 0, f"requires capacity to >= 0. cap={cap}, num_tokens={num_tokens}"
        return capacity


class Ernie4_5_RMSNorm(nn.Module):
    """
    Ernie Root Mean Square Layer Normalization (Ernie4_5_RMSNorm) implementation.

    Ernie4_5_RMSNorm is a simplified version of LayerNorm that focuses on the root mean square of inputs,
    omitting the mean-centering operation. This provides computational efficiency while maintaining
    good performance.

    """

    def __init__(self, config):
        """
        Initialize RMSNorm layer.

        Args:
            config (ErnieConfig): Model configuration.
        """
        super().__init__()
        self.config = config
        self.hidden_size = config.hidden_size
        self.weight = nn.Parameter(torch.ones(config.hidden_size))
        self.variance_epsilon = config.rms_norm_eps

    def forward(self, hidden_states):
        """
        Apply RMS normalization to input hidden states.

        Args:
            hidden_states (Tensor): Input tensor of shape [batch_size, seq_len, hidden_size]

        Returns:
            Tensor: Normalized output tensor of same shape as input
        """
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(dim=-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)

        return self.weight * hidden_states.to(input_dtype)


class Ernie4_5_RopeEmbedding(nn.Module):
    def __init__(self, config: Ernie4_5_MoeConfig, device=None):
        super().__init__()
        # BC: "rope_type" was originally "type"
        if hasattr(config, "rope_scaling") and config.rope_scaling is not None:
            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
        else:
            self.rope_type = "default"
        self.max_seq_len_cached = config.max_position_embeddings
        self.original_max_seq_len = config.max_position_embeddings

        self.config = config
        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
        self.register_buffer("inv_freq", inv_freq, persistent=False)
        self.original_inv_freq = self.inv_freq

    @torch.no_grad()
    def forward(self, x, position_ids):
        inv_freq_expanded = self.inv_freq[None,None,:].float()
        position_ids_expanded = position_ids[...,None].float()
        freqs = (inv_freq_expanded.float() * position_ids_expanded.float())
        cos = torch.cos(freqs) * self.attention_scaling
        sin = torch.sin(freqs) * self.attention_scaling
        return cos, sin
        # return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)


class Ernie4_5_DecoderLayer(nn.Module):
    """A single transformer decoder layer in ERNIE-MoE model.

    Contains self-attention and feed-forward components with optional MoE (Mixture of Experts)
    support, residual connections, and layer normalization.
    """

    def __init__(self, config, layer_idx):
        """Initialize the decoder layer.

        Args:
            config (ErnieMoEConfig): Model configuration.
            layer_idx (int): Index of this layer in the transformer stack
        """
        super().__init__()
        self.hidden_size = config.hidden_size
        self.layer_idx = layer_idx
        self.config = config
        self.use_moe = config.use_moe
        self.self_attn = Ernie4_5_Attention(config, layer_idx)

        moe_layer_start_index = (
            min(config.moe_layer_start_index)
            if isinstance(config.moe_layer_start_index, (tuple, list))
            else config.moe_layer_start_index
        )
        moe_layer_end_index = (
            max(config.moe_layer_end_index)
            if isinstance(config.moe_layer_end_index, (tuple, list))
            else config.moe_layer_end_index
        )

        if (
            self.use_moe
            and ((layer_idx + 1) % config.moe_layer_interval == 0)
            and layer_idx >= moe_layer_start_index
            and layer_idx <= moe_layer_end_index
        ):
            self.mlp = Ernie4_5_MoeMLP(config)
        else:
            self.mlp = Ernie4_5_MLP(config)

        self.input_layernorm = Ernie4_5_RMSNorm(config)
        self.post_attention_layernorm = Ernie4_5_RMSNorm(config)

        self.residual_add1 = Ernie4_5_ResidualWithDropout(config.hidden_dropout_prob)
        self.residual_add2 = Ernie4_5_ResidualWithDropout(config.hidden_dropout_prob)
    
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        cache_position: Optional[torch.LongTensor] = None,
        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,  # necessary, but kept here for BC
        output_router_loss: bool = True,
        output_gate_logits: bool = True,
        **kwargs: Unpack[FlashAttentionKwargs],
    ) -> tuple[torch.FloatTensor, Optional[tuple[torch.FloatTensor, torch.FloatTensor]]]:
        """Forward pass through the decoder layer.

        Args:
            hidden_states (torch.Tensor): Input tensor [batch_size, seq_len, hidden_size]
            attention_mask (Optional[torch.Tensor]): Attention mask tensor
            position_ids (Optional[torch.Tensor]): Position indices for rotary embeddings
            past_key_value (Optional[Tuple[torch.Tensor]]): Cached key/value states
            output_attentions (Optional[bool]): Whether to return attention weights
            use_cache (Optional[bool]): Whether to cache key/value states
            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
                Indices depicting the position of the input sequence tokens in the sequence.
            position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
                with `head_dim` being the embedding dimension of each attention head.
            output_router_loss (bool): Whether to return MoE router loss
            output_gate_logits (bool): Whether to return MoE gate logits

        Returns:
            Union: Various output combinations depending on arguments:
                - Base case: Hidden states tensor
                - With attention: Tuple of (hidden_states, attention_weights)
                - With router loss: May include gate logits in output tuple
                - With MoE gate logits: May include gate logits in output tuple
        """
        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        # Self Attention
        hidden_states, self_attn_weights = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            past_key_value=past_key_value,
            position_ids=position_ids,
            use_cache=use_cache,
            cache_position=cache_position,
            position_embeddings=position_embeddings,
            **kwargs,
        )

        hidden_states = self.residual_add1(hidden_states, residual)

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

        router_loss = None
        gate_logits = None

        if isinstance(self.mlp, Ernie4_5_MoeMLP):
            hidden_states, _, router_loss, gate_logits = self.mlp(hidden_states)
        else:
            hidden_states = self.mlp(hidden_states)

        hidden_states = self.residual_add2(hidden_states, residual)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if output_router_loss:
            outputs += (router_loss,)

        if output_gate_logits:
            outputs += (gate_logits,)

        return outputs


@auto_docstring
class Ernie4_5_PretrainedModel(PreTrainedModel):
    """Base class for ERNIE pretrained models."""
    config_class = Ernie4_5_MoeConfig
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["Ernie4_5_DecoderLayer"]
    _skip_keys_device_placement = ["past_key_values"]
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_flex_attn = True
    _supports_cache_class = True
    _supports_quantized_cache = True
    _supports_static_cache = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)


def subbatch(f, arg_idx, axis, bs, out_idx, same_arg_idx={}):
    """
    Converts a function to one that applies to subbatch of an input dimension.
    Useful for processing large tensors in smaller chunks to reduce memory usage.

    Args:
        f (Callable): Function to be subbatched.
        arg_idx ([int]): Indices of the inputs to be subbatched.
        axis ([int]): Indices of the dimensions to be subbatched for each input.
        bs (int): Subbatch size.
        out_idx (int): Dimension to concatenate outputs along.
        same_arg_idx (dict): Mapping of argument indices that share the same tensor.

    Returns:
        Callable: New function that processes inputs in subbatches.
    """

    @functools.wraps(f)
    def wrapper(*args, **kwargs):

        assert len(arg_idx) == len(axis), "Number of batching args and number of batching dims should match."

        inps = [args[i] for i in arg_idx]
        axis_width = [inp.shape[d] for inp, d in zip(inps, axis)]
        assert len(set(axis_width)) == 1, "Batch sizes should be kept equal."

        inp_axis = {idx: d for idx, d in zip(arg_idx, axis)}

        axis_width = axis_width[0]
        if axis_width < bs:
            return f(*args, **kwargs)

        outs = []
        for slice_at in range(0, axis_width, bs):
            _args = []
            for i, inp in enumerate(args):
                if i in same_arg_idx:
                    assert (
                        i > same_arg_idx[i]
                    ), f"expect i > same_arg_idx[i], but got i: {i} and same_arg_idx[i]: {same_arg_idx[i]}"
                    _args.append(_args[same_arg_idx[i]])
                elif i in arg_idx:
                    d = inp_axis[i]
                    start = slice_at
                    end = min(inp.shape[d], slice_at + bs)
                    # Build slice for all dims, only slice along axis d
                    slices = [slice(None)] * inp.ndim
                    slices[d] = slice(start, end)
                    _args.append(inp[tuple(slices)])
                else:
                    _args.append(inp)

            out = f(*_args, **kwargs)
            outs.append(out)

        return torch.cat(outs, dim=out_idx)

    return wrapper


class ErniePretrainingCriterion(nn.Module):
    """Criterion for ERNIE pretraining task."""

    def __init__(self, config, return_tuple=True):
        """Initialize the pretraining criterion.

        Args:
            config (ErnieConfig): Model configuration.
            return_tuple (bool): Whether to return loss as tuple (loss, loss_sum). Defaults to True.
        """
        super().__init__()
        self.ignored_index = getattr(config, "ignored_index", -100)
        self.config = config
        self.return_tuple = return_tuple
    
        self.loss_func = nn.CrossEntropyLoss(reduction="none")

    def forward(self, prediction_scores, masked_lm_labels, loss_mask, router_loss=None, mtp_logits=None):
        """Compute the combined pretraining loss.

        Args:
            prediction_scores: Prediction scores tensor, [batch_size, seq_len, vocab_size]
            masked_lm_labels: Target labels tensor [batch_size, seq_len]
            loss_mask: Optional mask for valid tokens
            router_loss: Optional MoE router loss tensor

        Returns:
            Union:
                - If return_tuple=True: Tuple of (combined_loss, mlm_loss_sum)
                - If return_tuple=False: Combined loss tensor
        """
        if self.config.num_nextn_predict_layers > 0 and self.training:
            masked_lm_labels_ori = masked_lm_labels
            masked_lm_labels = masked_lm_labels[:, : -self.config.num_nextn_predict_layers]
            loss_mask = loss_mask[:, : -self.config.num_nextn_predict_layers]
            seq_length = masked_lm_labels.shape[1]

        res = self.forward_impl(prediction_scores, masked_lm_labels, loss_mask)

        if self.config.num_nextn_predict_layers > 0 and self.training:
            mtp_loss_res = []
            for depth in range(self.config.num_nextn_predict_layers):
                prediction_scores_cur_depth = mtp_logits[depth]
                masked_lm_labels_cur_depth = masked_lm_labels_ori[:, (depth + 1) : (depth + 1 + seq_length)]
                res_cur_depth = super().forward(
                    prediction_scores_cur_depth,
                    masked_lm_labels_cur_depth,
                )
                mtp_loss_res.append(res_cur_depth)

        def add_loss(main_loss, loss):
            return main_loss + loss - loss.detach()
        

        if self.return_tuple:
            loss, loss_sum = res
            if self.config.num_nextn_predict_layers > 0 and self.training:
                loss = add_loss(
                    loss, self.config.multi_token_pred_lambda * sum([x[0] for x in mtp_loss_res]) / len(mtp_loss_res)
                )
                loss_sum = loss_sum + self.config.multi_token_pred_lambda * sum(
                    [x[1].detach() for x in mtp_loss_res]
                ) / len(mtp_loss_res)
        else:
            loss, loss_sum = res, None
            if self.config.num_nextn_predict_layers > 0 and self.training:
                loss = add_loss(
                    loss, self.config.multi_token_pred_lambda * sum([x[0] for x in mtp_loss_res]) / len(mtp_loss_res)
                )

        if router_loss is not None and isinstance(router_loss, torch.Tensor):
            loss = loss + router_loss - router_loss.detach()

        return loss, loss_sum


    def loss_impl(self, prediction_scores: torch.Tensor, masked_lm_labels: torch.Tensor) -> torch.Tensor:
        """
        Core loss computation without reduction (but per-token).

        Args:
            prediction_scores (torch.Tensor): Logits tensor [batch_size, seq_len, vocab_size].
            masked_lm_labels (torch.Tensor): Target labels tensor [batch_size, seq_len].

        Returns:
            torch.Tensor: Unreduced loss tensor of shape [batch_size, seq_len].
                          Losses are calculated in float32.
        """
        scores_float32 = prediction_scores.to(torch.float32)
        # prediction_scores: [batch_size, seq_len, vocab_size]
        # masked_lm_labels: [batch_size, seq_len]
        # Transpose prediction_scores to [batch_size, vocab_size, seq_len]
        unreduced_loss = self.loss_func(
            scores_float32.transpose(1, 2),  # Shape: [batch_size, vocab_size, seq_len]
            masked_lm_labels.long()          # Shape: [batch_size, seq_len], ensure long type
        )
        # unreduced_loss will be of shape [batch_size, seq_len] and dtype float32
        return unreduced_loss

    def forward_impl(self, prediction_scores, masked_lm_labels, loss_mask=None):
        prediction_scores_dims = len(prediction_scores.shape)
        
        loss_subbatch_seqlen_config_key = "loss_subbatch_seqlen"
        default_loss_subbatch_seqlen = 32768

        current_loss_subbatch_seqlen = self.config.get(
            loss_subbatch_seqlen_config_key, default_loss_subbatch_seqlen
        )

        if prediction_scores_dims == 2 and prediction_scores.shape[0] > current_loss_subbatch_seqlen:
            sb_loss_func = subbatch(
                self.loss_impl, [0, 1], [0, 0], current_loss_subbatch_seqlen, 0
            )
            masked_lm_loss = sb_loss_func(prediction_scores, masked_lm_labels)
        elif prediction_scores_dims == 3 and prediction_scores.shape[1] > current_loss_subbatch_seqlen:
            sb_loss_func = subbatch(
                self.loss_impl, [0, 1], [1, 1], current_loss_subbatch_seqlen, 1
            )
            masked_lm_loss = sb_loss_func(prediction_scores, masked_lm_labels)
        else:
            masked_lm_loss = self.loss_impl(prediction_scores, masked_lm_labels)

        if loss_mask is None:
            loss_mask = masked_lm_labels != self.ignored_index

        loss_mask = loss_mask.reshape(-1).to(torch.float32)

        masked_lm_loss = torch.sum(masked_lm_loss.to(torch.float32).reshape(-1) * loss_mask)
        
        # The division will be in float32
        loss = masked_lm_loss / loss_mask.sum()
        
        loss_sum = masked_lm_loss.sum().detach()

        if not self.return_tuple:
            if self.training:
                return loss
            return loss_sum
        return loss, loss_sum

@auto_docstring
class Ernie4_5_Model(Ernie4_5_PretrainedModel):
    """The core ERNIE transformer model with MoE (Mixture of Experts) support."""
    _keep_in_fp32_modules = ['gate']
    def __init__(self, config: Ernie4_5_MoeConfig):
        """Initialize the ERNIE model architecture."""
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.hidden_size = config.hidden_size
        self.config = config

        self.embed_tokens = nn.Embedding(
            self.vocab_size,
            self.hidden_size,
        )

        self.layers = nn.ModuleList(
            [
                Ernie4_5_DecoderLayer(config, i)
                for i in range(config.num_hidden_layers)
            ]
        )
        self.norm = Ernie4_5_RMSNorm(config)
        self.rotary_emb = Ernie4_5_RopeEmbedding(config=config)

        self.gradient_checkpointing = False

        if config.num_nextn_predict_layers > 0 and self.training:
            self.mtp_block = nn.ModuleList(
                [Ernie4_5_DecoderLayer(config, layer_idx) for layer_idx in range(config.num_nextn_predict_layers)]
            )
            self.mtp_emb_norm = nn.ModuleList(
                [Ernie4_5_RMSNorm(config) for _ in range(config.num_nextn_predict_layers)]
            )
            self.mtp_hidden_norm = nn.ModuleList(
                [Ernie4_5_RMSNorm(config) for _ in range(config.num_nextn_predict_layers)]
            )
            self.mtp_linear_proj = nn.ModuleList(
                [nn.Linear(config.hidden_size * 2, config.hidden_size, bias=config.use_bias) for _ in range(config.num_nextn_predict_layers)]
            )

        self.post_init()

    def get_input_embeddings(self):
        """Get the input embedding layer."""
        return self.embed_tokens

    def set_input_embeddings(self, value):
        """Set new input embeddings."""
        self.embed_tokens = value

    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,
        **flash_attn_kwargs: Unpack[FlashAttentionKwargs],
    ):
        """Forward pass through the ERNIE model."""
        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
        )
        
        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

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

        if use_cache and past_key_values is None:
            past_key_values = DynamicCache()

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

        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)
        
        seq_length = inputs_embeds.size(1)
        if self.config.num_nextn_predict_layers > 0 and self.training:
            seq_length -= self.config.num_nextn_predict_layers
            seq_length_with_past = seq_length
            if position_ids is not None:
                position_ids = position_ids[:, :seq_length]
            inputs_embeds_extra = inputs_embeds[:, -self.config.num_nextn_predict_layers :, :]
            inputs_embeds = inputs_embeds[:, : -self.config.num_nextn_predict_layers, :]
            inputs_embeds_ori = inputs_embeds

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

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

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        all_router_loss = torch.tensor(0.0, device=inputs_embeds.device) if self.config.use_moe else None
        all_gate_logits = ()

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

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

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)
            
            if self.config.use_moe:
                layer_outputs, gate_logits = layer_outputs[:-1], layer_outputs[-1]
                all_gate_logits = all_gate_logits + (gate_logits,)
        
        mtp_outputs = []
        if self.config.num_nextn_predict_layers > 0 and self.training:
            mtp_outputs.append(hidden_states)
            for depth in range(self.config.num_nextn_predict_layers):
                inputs_embeds_cur_depth = torch.concat(
                    [inputs_embeds_ori[:, (depth + 1) :, :], inputs_embeds_extra[:, : (depth + 1), :]], axis=1
                )
                inputs_embeds_cur_depth_norm = self.mtp_emb_norm[depth](inputs_embeds_cur_depth)
                hidden_states_norm = self.mtp_hidden_norm[depth](hidden_states)
                
                inputs_embeds_cur_depth = self.mtp_linear_proj[depth](
                    torch.concat([inputs_embeds_cur_depth_norm, hidden_states_norm], axis=-1)
                )

                decoder_layer = self.mtp_block[depth]
                layer_outputs = decoder_layer(
                    inputs_embeds_cur_depth,
                    causal_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                    cache_position,
                    position_embeddings,
                    **flash_attn_kwargs,
                )
                if isinstance(layer_outputs, (tuple, list)):
                    hidden_states = layer_outputs[0]
                else:
                    hidden_states = layer_outputs

                if self.config.use_moe:
                    layer_outputs, gate_logits = layer_outputs[:-1], layer_outputs[-1]
                    all_gate_logits = all_gate_logits + (gate_logits,)

                mtp_outputs.append(hidden_states)
            mtp_outputs = [self.norm(hidden_states) for depth, hidden_states in enumerate(mtp_outputs)]
            hidden_states, mtp_outputs = mtp_outputs[0], mtp_outputs[1:]
        else:
            hidden_states = self.norm(hidden_states)

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

        # assert all_router_loss is None, f'moe not support `return-dict`'
        return Erine4_5_MoeModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            router_loss=all_router_loss,
            gate_logits=all_gate_logits,
            mtp_outputs=mtp_outputs,
        )

    def _update_causal_mask(
        self,
        attention_mask: Union[torch.Tensor, "BlockMask"],
        input_tensor: torch.Tensor,
        cache_position: torch.Tensor,
        past_key_values: Cache,
        output_attentions: bool = False,
    ):
        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and past_key_values is not None:
                is_padding_right = attention_mask[:, -1].sum().item() != input_tensor.size()[0]
                if is_padding_right:
                    raise ValueError(
                        "You are attempting to perform batched generation with padding_side='right'"
                        " this may lead to unexpected behaviour for Flash Attention version of Qwen3. Make sure to "
                        " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
                    )
            if attention_mask is not None and 0.0 in attention_mask:
                return attention_mask
            return None
        if self.config._attn_implementation == "flex_attention":
            if isinstance(attention_mask, torch.Tensor):
                attention_mask = make_flex_block_causal_mask(attention_mask)
            return attention_mask

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

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

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

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

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

        return causal_mask

    @staticmethod
    def _prepare_4d_causal_attention_mask_with_cache_position(
        attention_mask: torch.Tensor,
        sequence_length: int,
        target_length: int,
        dtype: torch.dtype,
        cache_position: torch.Tensor,
        batch_size: int,
        config: Ernie4_5_MoeConfig,
        past_key_values: Cache,
    ):
        """
        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.

        Args:
            attention_mask (`torch.Tensor`):
                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
            sequence_length (`int`):
                The sequence length being processed.
            target_length (`int`):
                The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
            dtype (`torch.dtype`):
                The dtype to use for the 4D attention mask.
            cache_position (`torch.Tensor`):
                Indices depicting the position of the input sequence tokens in the sequence.
            batch_size (`torch.Tensor`):
                Batch size.
            config (`Ernie4_5_MoeConfig`):
                The model's configuration class
            past_key_values (`Cache`):
                The cache class that is being used currently to generate
        """
        if attention_mask is not None and attention_mask.dim() == 4:
            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
            causal_mask = attention_mask
        else:
            min_dtype = torch.finfo(dtype).min
            causal_mask = torch.full(
                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=cache_position.device
            )
            diagonal_attend_mask = torch.arange(target_length, device=cache_position.device) > cache_position.reshape(
                -1, 1
            )
            text_config = config.get_text_config()
            if getattr(text_config, "use_sliding_window", True) and text_config.sliding_window is not None:
                # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
                # the check is needed to verify is current checkpoint was trained with sliding window or not
                if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
                    sliding_attend_mask = torch.arange(target_length, device=cache_position.device) <= (
                        cache_position.reshape(-1, 1) - text_config.sliding_window
                    )
                    diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
            causal_mask *= diagonal_attend_mask
            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
            if attention_mask is not None:
                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                if attention_mask.shape[-1] > target_length:
                    attention_mask = attention_mask[:, :target_length]
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :].to(
                    causal_mask.device
                )
                padding_mask = padding_mask == 0
                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                    padding_mask, min_dtype
                )
        return causal_mask

@auto_docstring
class Ernie4_5_MoeForCausalLM(Ernie4_5_PretrainedModel,GenerationMixin):
    """ERNIE Mixture of Experts (MoE) model for causal language modeling."""

    _tied_weights_keys = ["lm_head.weight"]
    _tp_plan = {"lm_head": "colwise_rep"}
    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}

    def __init__(self, config):
        """
        Initializes the ERNIE MoE model for causal language modeling.

        Args:
            config (dict): Model configuration.
        """
        super().__init__(config)
        self.config = config
        self.model = Ernie4_5_Model(config)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size,bias=config.weight_share_add_bias and config.use_bias) # TODO
        self.loss_function = ErniePretrainingCriterion(config)

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

    def get_input_embeddings(self):
        """Returns the input embeddings layer."""
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        """Sets the input embeddings layer."""
        self.ernie.embed_tokens = value

    def get_output_embeddings(self):
        """Returns the output embeddings (LM head)."""
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        """Sets the output embeddings layer."""
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        """Sets the ERNIE decoder model."""
        self.model = decoder

    def get_decoder(self):
        """Get the transformer decoder."""
        return self.model

    @can_return_tuple
    def forward(
        self,
        input_ids,
        attention_mask=None,
        position_ids=None,
        past_key_values: Optional[list[torch.FloatTensor]] = None,
        inputs_embeds=None,
        labels=None,
        loss_mask=None,
        use_cache=False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        **kwargs: Unpack[KwargsForCausalLM],
    ):
        """
        Forward pass for causal language modeling.
        """
        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
        )

        outputs = self.model(
            input_ids,
            position_ids=position_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            past_key_values=past_key_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            **kwargs,
        )

        hidden_states = outputs.last_hidden_state
        mtp_outputs = outputs.mtp_outputs

        logits = self.lm_head(hidden_states)
        mtp_logits = []
        if len(mtp_outputs) > 0:
            mtp_logits = [self.lm_head(_hidden_states) for _hidden_states in mtp_outputs]
        loss, router_loss = None, None
        if getattr(self.config, "use_moe", False):
            router_loss = outputs.router_loss

        if labels is not None:
            loss, _ = self.loss_function(logits, labels, loss_mask, router_loss, mtp_logits)

        return Ernie4_5_MoeCausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            router_loss=router_loss,
        )



__all__ = [
    "Ernie4_5_Model",
    "Ernie4_5_MoeForCausalLM",
    "Ernie4_5_PretrainedModel"
]