lmstudio-community
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ERNIE-4.5-21B-A3B-MLX-4bit

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-# 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, 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 = getattr(self.config, 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 Ernie4_5. 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)
-        # 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
-            and not output_attentions
-        ):
-            if AttentionMaskConverter._ignore_causal_mask_sdpa(
-                attention_mask,
-                inputs_embeds=input_tensor,
-                past_key_values_length=past_seen_tokens,
-                is_training=self.training,
-            ):
-                return None
-        dtype = input_tensor.dtype
-        min_dtype = torch.finfo(dtype).min
-        sequence_length = input_tensor.shape[1]
-        # StaticCache
-        if 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()
-            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"
-]