Delete modeling_upcycling_qwen2_moe.py

modeling_upcycling_qwen2_moe.py

@@ -1,1672 +0,0 @@
-# coding=utf-8
-# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
-#
-# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
-# and OPT implementations in this library. It has been modified from its
-# original forms to accommodate minor architectural differences compared
-# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
-#
-# 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.
-"""PyTorch Qwen2MoE model."""
-
-import inspect
-import math
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn.functional as F
-import torch.utils.checkpoint
-from torch import nn
-from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
-
-from transformers.activations import ACT2FN
-from transformers.cache_utils import Cache, DynamicCache, StaticCache
-from transformers.modeling_attn_mask_utils import (
-    AttentionMaskConverter,
-)
-from transformers.modeling_outputs import (
-    MoeCausalLMOutputWithPast,
-    MoeModelOutputWithPast,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.utils import (
-    is_flash_attn_2_available,
-    is_flash_attn_greater_or_equal_2_10,
-    logging,
-    replace_return_docstrings,
-)
-from transformers_modules.FreedomIntelligence.Apollo-MoE-7B.fdfe57b4c6087b0ec4f6d16fdb6aad6f9ce6fbd7.configuration_upcycling_qwen2_moe import UpcyclingQwen2MoeConfig
-from transformers import AutoModelForCausalLM,AutoConfig,AutoModel
-
-
-if is_flash_attn_2_available():
-    from flash_attn import flash_attn_func, flash_attn_varlen_func
-    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
-
-    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "UpcyclingQwen2MoE"
-_CONFIG_FOR_DOC = "UpcyclingQwen2MoeConfig"
-
-
-# Copied from transformers.models.mixtral.modeling_mixtral.load_balancing_loss_func
-def load_balancing_loss_func(
-    gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2, attention_mask: Optional[torch.Tensor] = None
-) -> float:
-    r"""
-    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
-
-    See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
-    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
-    experts is too unbalanced.
-
-    Args:
-        gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
-            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
-            shape [batch_size X sequence_length, num_experts].
-        attention_mask (`torch.Tensor`, None):
-            The attention_mask used in forward function
-            shape [batch_size X sequence_length] if not None.
-        num_experts (`int`, *optional*):
-            Number of experts
-
-    Returns:
-        The auxiliary loss.
-    """
-    if gate_logits is None or not isinstance(gate_logits, tuple):
-        return 0
-
-    if isinstance(gate_logits, tuple):
-        compute_device = gate_logits[0].device
-        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
-
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
-
-    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
-
-    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
-
-    if attention_mask is None:
-        # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
-
-        # Compute the average probability of routing to these experts
-        router_prob_per_expert = torch.mean(routing_weights, dim=0)
-    else:
-        batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
-
-        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
-        expert_attention_mask = (
-            attention_mask[None, :, :, None, None]
-            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
-            .reshape(-1, top_k, num_experts)
-            .to(compute_device)
-        )
-
-        # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
-        )
-
-        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
-        router_per_expert_attention_mask = (
-            attention_mask[None, :, :, None]
-            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
-            .reshape(-1, num_experts)
-            .to(compute_device)
-        )
-
-        # Compute the average probability of routing to these experts
-        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
-            router_per_expert_attention_mask, dim=0
-        )
-
-    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
-    return overall_loss * num_experts
-
-
-# Copied from transformers.models.llama.modeling_llama._get_unpad_data
-def _get_unpad_data(attention_mask):
-    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
-    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max().item()
-    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Qwen2Moe
-class Qwen2MoeRMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        Qwen2MoeRMSNorm is equivalent to T5LayerNorm
-        """
-        super().__init__()
-        self.weight = nn.Parameter(torch.ones(hidden_size))
-        self.variance_epsilon = eps
-
-    def forward(self, hidden_states):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(torch.float32)
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-
-
-# Copied from transformers.models.mixtral.modeling_mixtral.MixtralRotaryEmbedding with Mixtral->Qwen2Moe
-class Qwen2MoeRotaryEmbedding(nn.Module):
-    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
-        super().__init__()
-
-        self.dim = dim
-        self.max_position_embeddings = max_position_embeddings
-        self.base = base
-        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        # Build here to make `torch.jit.trace` work.
-        self._set_cos_sin_cache(
-            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
-        )
-
-    def _set_cos_sin_cache(self, seq_len, device, dtype):
-        self.max_seq_len_cached = seq_len
-        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
-
-        freqs = torch.outer(t, self.inv_freq)
-        # Different from paper, but it uses a different permutation in order to obtain the same calculation
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
-
-    def forward(self, x, seq_len=None):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        if seq_len > self.max_seq_len_cached:
-            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
-
-        return (
-            self.cos_cached[:seq_len].to(dtype=x.dtype),
-            self.sin_cached[:seq_len].to(dtype=x.dtype),
-        )
-
-
-# Copied from transformers.models.llama.modeling_llama.rotate_half
-def rotate_half(x):
-    """Rotates half the hidden dims of the input."""
-    x1 = x[..., : x.shape[-1] // 2]
-    x2 = x[..., x.shape[-1] // 2 :]
-    return torch.cat((-x2, x1), dim=-1)
-
-
-# Copied from transformers.models.mixtral.modeling_mixtral.apply_rotary_pos_emb
-def apply_rotary_pos_emb(q, k, cos, sin, position_ids, 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`):
-            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
-            used to pass offsetted position ids when working with a KV-cache.
-        unsqueeze_dim (`int`, *optional*, defaults to 1):
-            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
-            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
-            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
-            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
-            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
-            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
-    Returns:
-        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
-    """
-    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
-    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-# Modified from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Qwen2Moe
-class Qwen2MoeMLP(nn.Module):
-    def __init__(self, config, intermediate_size=None):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size
-        self.intermediate_size = intermediate_size
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
-        self.act_fn = ACT2FN[config.hidden_act]
-
-    def forward(self, x,language_ids:Optional[torch.LongTensor]=None):
-        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-
-
-# Copied from transformers.models.llama.modeling_llama.repeat_kv
-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)
-
-
-# Copied from transformers.models.qwen2.modeling_qwen2.Qwen2Attention with Qwen2->Qwen2Moe
-class Qwen2MoeAttention(nn.Module):
-    """
-    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
-    and "Generating Long Sequences with Sparse Transformers".
-    """
-
-    def __init__(self, config: UpcyclingQwen2MoeConfig, layer_idx: Optional[int] = None):
-        super().__init__()
-        self.config = config
-        self.layer_idx = layer_idx
-        if layer_idx is None:
-            logger.warning_once(
-                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
-                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
-                "when creating this class."
-            )
-
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-        self.head_dim = self.hidden_size // self.num_heads
-        self.num_key_value_heads = config.num_key_value_heads
-        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
-        self.max_position_embeddings = config.max_position_embeddings
-        self.rope_theta = config.rope_theta
-        self.is_causal = True
-        self.attention_dropout = config.attention_dropout
-
-        if (self.head_dim * self.num_heads) != self.hidden_size:
-            raise ValueError(
-                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
-                f" and `num_heads`: {self.num_heads})."
-            )
-        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
-        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
-        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
-        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
-
-        self.rotary_emb = Qwen2MoeRotaryEmbedding(
-            self.head_dim,
-            max_position_embeddings=self.max_position_embeddings,
-            base=self.rope_theta,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            if self.layer_idx is None:
-                raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
-                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
-                    "with a layer index."
-                )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # repeat k/v heads if n_kv_heads < n_heads
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
-            raise ValueError(
-                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
-                f" {attn_weights.size()}"
-            )
-
-        if attention_mask is not None:  # no matter the length, we just slice it
-            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-            attn_weights = attn_weights + causal_mask
-
-        # upcast attention to fp32
-        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
-        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
-        attn_output = torch.matmul(attn_weights, value_states)
-
-        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
-                f" {attn_output.size()}"
-            )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
-
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-
-# Copied from transformers.models.qwen2.modeling_qwen2.Qwen2FlashAttention2 with Qwen2->Qwen2Moe
-class Qwen2MoeFlashAttention2(Qwen2MoeAttention):
-    """
-    Qwen2Moe flash attention module, following Qwen2Moe attention module. This module inherits from `Qwen2MoeAttention`
-    as the weights of the module stays untouched. The only required change would be on the forward pass
-    where it needs to correctly call the public API of flash attention and deal with padding tokens
-    in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom
-    config.max_window_layers layers.
-    """
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
-        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
-        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
-        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ):
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            if self.layer_idx is None:
-                raise ValueError(
-                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
-                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
-                    "with a layer index."
-                )
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-
-        # Because the input can be padded, the absolute sequence length depends on the max position id.
-        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
-        cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
-
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
-        use_sliding_windows = (
-            _flash_supports_window_size
-            and getattr(self.config, "sliding_window", None) is not None
-            and kv_seq_len > self.config.sliding_window
-            and self.config.use_sliding_window
-        )
-
-        if not _flash_supports_window_size:
-            logger.warning_once(
-                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
-                " make sure to upgrade flash-attn library."
-            )
-
-        if past_key_value is not None:
-            # Activate slicing cache only if the config has a value `sliding_windows` attribute
-            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
-            if (
-                getattr(self.config, "sliding_window", None) is not None
-                and kv_seq_len > self.config.sliding_window
-                and cache_has_contents
-            ):
-                slicing_tokens = 1 - self.config.sliding_window
-
-                past_key = past_key_value[self.layer_idx][0]
-                past_value = past_key_value[self.layer_idx][1]
-
-                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
-                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
-
-                if past_key.shape[-2] != self.config.sliding_window - 1:
-                    raise ValueError(
-                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
-                        f" {past_key.shape}"
-                    )
-
-                if attention_mask is not None:
-                    attention_mask = attention_mask[:, slicing_tokens:]
-                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
-
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        # repeat k/v heads if n_kv_heads < n_heads
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-        dropout_rate = 0.0 if not self.training else self.attention_dropout
-
-        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
-        # therefore the input hidden states gets silently casted in float32. Hence, we need
-        # cast them back in float16 just to be sure everything works as expected.
-        input_dtype = query_states.dtype
-        if input_dtype == torch.float32:
-            if torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            # Handle the case where the model is quantized
-            elif hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            else:
-                target_dtype = self.q_proj.weight.dtype
-
-            logger.warning_once(
-                f"The input hidden states seems to be silently casted in float32, this might be related to"
-                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
-                f" {target_dtype}."
-            )
-
-            query_states = query_states.to(target_dtype)
-            key_states = key_states.to(target_dtype)
-            value_states = value_states.to(target_dtype)
-
-        # Reashape to the expected shape for Flash Attention
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        attn_output = self._flash_attention_forward(
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            q_len,
-            dropout=dropout_rate,
-            use_sliding_windows=use_sliding_windows,
-        )
-
-        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
-        attn_output = self.o_proj(attn_output)
-
-        if not output_attentions:
-            attn_weights = None
-
-        return attn_output, attn_weights, past_key_value
-
-    def _flash_attention_forward(
-        self,
-        query_states,
-        key_states,
-        value_states,
-        attention_mask,
-        query_length,
-        dropout=0.0,
-        softmax_scale=None,
-        use_sliding_windows=False,
-    ):
-        """
-        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
-        first unpad the input, then computes the attention scores and pad the final attention scores.
-
-        Args:
-            query_states (`torch.Tensor`):
-                Input query states to be passed to Flash Attention API
-            key_states (`torch.Tensor`):
-                Input key states to be passed to Flash Attention API
-            value_states (`torch.Tensor`):
-                Input value states to be passed to Flash Attention API
-            attention_mask (`torch.Tensor`):
-                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
-                position of padding tokens and 1 for the position of non-padding tokens.
-            dropout (`float`):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-            use_sliding_windows (`bool`, *optional*):
-                Whether to activate sliding window attention.
-        """
-        if not self._flash_attn_uses_top_left_mask:
-            causal = self.is_causal
-        else:
-            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
-            causal = self.is_causal and query_length != 1
-
-        # Decide whether to use SWA or not by layer index.
-        if use_sliding_windows and self.layer_idx >= self.config.max_window_layers:
-            use_sliding_windows = False
-
-        # Contains at least one padding token in the sequence
-        if attention_mask is not None:
-            batch_size = query_states.shape[0]
-            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
-                query_states, key_states, value_states, attention_mask, query_length
-            )
-
-            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
-            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
-
-            if not use_sliding_windows:
-                attn_output_unpad = flash_attn_varlen_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    cu_seqlens_q=cu_seqlens_q,
-                    cu_seqlens_k=cu_seqlens_k,
-                    max_seqlen_q=max_seqlen_in_batch_q,
-                    max_seqlen_k=max_seqlen_in_batch_k,
-                    dropout_p=dropout,
-                    softmax_scale=softmax_scale,
-                    causal=causal,
-                )
-            else:
-                attn_output_unpad = flash_attn_varlen_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    cu_seqlens_q=cu_seqlens_q,
-                    cu_seqlens_k=cu_seqlens_k,
-                    max_seqlen_q=max_seqlen_in_batch_q,
-                    max_seqlen_k=max_seqlen_in_batch_k,
-                    dropout_p=dropout,
-                    softmax_scale=softmax_scale,
-                    causal=causal,
-                    window_size=(self.config.sliding_window, self.config.sliding_window),
-                )
-
-            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
-        else:
-            if not use_sliding_windows:
-                attn_output = flash_attn_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    dropout,
-                    softmax_scale=softmax_scale,
-                    causal=causal,
-                )
-            else:
-                attn_output = flash_attn_func(
-                    query_states,
-                    key_states,
-                    value_states,
-                    dropout,
-                    softmax_scale=softmax_scale,
-                    causal=causal,
-                    window_size=(self.config.sliding_window, self.config.sliding_window),
-                )
-
-        return attn_output
-
-    # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
-    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
-        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
-
-        # On the first iteration we need to properly re-create the padding mask
-        # by slicing it on the proper place
-        if kv_seq_len != attention_mask.shape[-1]:
-            attention_mask_num_tokens = attention_mask.shape[-1]
-            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
-
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
-
-        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
-        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
-
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(
-                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
-            )
-            cu_seqlens_q = cu_seqlens_k
-            max_seqlen_in_batch_q = max_seqlen_in_batch_k
-            indices_q = indices_k
-        elif query_length == 1:
-            max_seqlen_in_batch_q = 1
-            cu_seqlens_q = torch.arange(
-                batch_size + 1, dtype=torch.int32, device=query_layer.device
-            )  # There is a memcpy here, that is very bad.
-            indices_q = cu_seqlens_q[:-1]
-            query_layer = query_layer.squeeze(1)
-        else:
-            # The -q_len: slice assumes left padding.
-            attention_mask = attention_mask[:, -query_length:]
-            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
-
-        return (
-            query_layer,
-            key_layer,
-            value_layer,
-            indices_q,
-            (cu_seqlens_q, cu_seqlens_k),
-            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
-        )
-
-
-# Copied from transformers.models.mixtral.modeling_mixtral.MixtralSdpaAttention with Mixtral->Qwen2Moe
-class Qwen2MoeSdpaAttention(Qwen2MoeAttention):
-    """
-    Qwen2Moe attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
-    `Qwen2MoeAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
-    SDPA API.
-    """
-
-    # Adapted from Qwen2MoeAttention.forward
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if output_attentions:
-            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
-            logger.warning_once(
-                "Qwen2MoeModel is using Qwen2MoeSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
-                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
-            )
-            return super().forward(
-                hidden_states=hidden_states,
-                attention_mask=attention_mask,
-                position_ids=position_ids,
-                past_key_value=past_key_value,
-                output_attentions=output_attentions,
-                use_cache=use_cache,
-            )
-
-        bsz, q_len, _ = hidden_states.size()
-
-        query_states = self.q_proj(hidden_states)
-        key_states = self.k_proj(hidden_states)
-        value_states = self.v_proj(hidden_states)
-
-        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
-
-        kv_seq_len = key_states.shape[-2]
-        if past_key_value is not None:
-            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
-        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
-
-        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
-
-        key_states = repeat_kv(key_states, self.num_key_value_groups)
-        value_states = repeat_kv(value_states, self.num_key_value_groups)
-
-        causal_mask = attention_mask
-        if attention_mask is not None:  # no matter the length, we just slice it
-            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
-
-        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
-        # Reference: https://github.com/pytorch/pytorch/issues/112577.
-        if query_states.device.type == "cuda" and attention_mask is not None:
-            query_states = query_states.contiguous()
-            key_states = key_states.contiguous()
-            value_states = value_states.contiguous()
-
-        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
-        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
-        # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
-        is_causal = True if causal_mask is None and q_len > 1 else False
-
-        attn_output = torch.nn.functional.scaled_dot_product_attention(
-            query_states,
-            key_states,
-            value_states,
-            attn_mask=causal_mask,
-            dropout_p=self.attention_dropout if self.training else 0.0,
-            is_causal=is_causal,
-        )
-
-        attn_output = attn_output.transpose(1, 2).contiguous()
-        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
-
-        attn_output = self.o_proj(attn_output)
-
-        return attn_output, None, past_key_value
-
-
-QWEN2MOE_ATTENTION_CLASSES = {
-    "eager": Qwen2MoeAttention,
-    "flash_attention_2": Qwen2MoeFlashAttention2,
-    "sdpa": Qwen2MoeSdpaAttention,
-}
-
-
-class Qwen2MoeSparseMoeBlock(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.num_experts = config.num_experts
-        self.top_k = config.num_experts_per_tok
-        self.norm_topk_prob = config.norm_topk_prob
-
-        # gating
-        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)
-        self.experts = nn.ModuleList(
-            [Qwen2MoeMLP(config, intermediate_size=config.moe_intermediate_size) for _ in range(self.num_experts)]
-        )
-        #share
-        self.share_flag=config.share_flag
-
-        if self.share_flag:
-            self.shared_expert = Qwen2MoeMLP(config, intermediate_size=config.shared_expert_intermediate_size)
-            self.shared_expert_gate = torch.nn.Linear(config.hidden_size, 1, bias=False)
-
-        #language-specific
-        self.language_gate=config.language_gate
-
-    def forward(self, hidden_states: torch.Tensor,language_ids:Optional[torch.LongTensor] = None) -> torch.Tensor:
-        
-        batch_size, sequence_length, hidden_dim = hidden_states.shape
-        hidden_states = hidden_states.view(-1, hidden_dim)
-        if self.language_gate and self.training :
-            if language_ids is None:
-                raise ValueError('language_ids is not initialized')
-            language_ids=language_ids.view(batch_size*sequence_length,-1)
-        # router_logits: (batch * sequence_length, n_experts)
-        router_logits = self.gate(hidden_states)
-        
-        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
-
-        _, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
-
-        #language specific select one expert
-        if self.language_gate and self.training:
-            if language_ids is None:
-                raise ValueError('language_ids is not initialized')
-            assert language_ids.shape[0]==selected_experts.shape[0],f'{language_ids.shape},{selected_experts.shape}'
-            language_experts=language_ids.to(selected_experts.dtype)
-            mask=torch.sum((language_experts==selected_experts).int(),dim=1,keepdims=True).bool()
-            selected_experts[:,-1]=torch.where(mask.squeeze(),selected_experts[:,-1],language_experts.squeeze())
-            routing_weights=torch.gather(routing_weights,1,selected_experts)
-        else:
-            routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
-
-        if self.norm_topk_prob:
-            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
-        # we cast back to the input dtype
-        routing_weights = routing_weights.to(hidden_states.dtype)
-
-        final_hidden_states = torch.zeros(
-            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
-        )
-
-        # One hot encode the selected experts to create an expert mask
-        # this will be used to easily index which expert is going to be sollicitated
-        expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
-
-        # Loop over all available experts in the model and perform the computation on each expert
-        for expert_idx in range(self.num_experts):
-            expert_layer = self.experts[expert_idx]
-            idx, top_x = torch.where(expert_mask[expert_idx])
-
-            # Index the correct hidden states and compute the expert hidden state for
-            # the current expert. We need to make sure to multiply the output hidden
-            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
-            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
-            current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
-
-            # However `index_add_` only support torch tensors for indexing so we'll use
-            # the `top_x` tensor here.
-            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
-
-        if self.share_flag:
-
-            shared_expert_output = self.shared_expert(hidden_states)
-            shared_expert_output = F.sigmoid(self.shared_expert_gate(hidden_states)) * shared_expert_output
-
-            final_hidden_states = final_hidden_states + shared_expert_output
-
-        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
-        return final_hidden_states, router_logits
-
-
-class Qwen2MoeDecoderLayer(nn.Module):
-    def __init__(self, config: UpcyclingQwen2MoeConfig, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-
-        self.self_attn = QWEN2MOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
-
-        if (layer_idx not in config.mlp_only_layers) and (
-            config.num_experts > 0 and (layer_idx + 1) % config.decoder_sparse_step == 0
-        ):
-            self.mlp = Qwen2MoeSparseMoeBlock(config)
-        else:
-            self.mlp = Qwen2MoeMLP(config, intermediate_size=config.intermediate_size)
-
-        self.input_layernorm = Qwen2MoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        self.post_attention_layernorm = Qwen2MoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        language_ids:Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        output_attentions: Optional[bool] = False,
-        output_router_logits: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
-        """
-        Args:
-            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
-            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
-                `(batch, sequence_length)` where padding elements are indicated by 0.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            output_router_logits (`bool`, *optional*):
-                Whether or not to return the logits of all the routers. They are useful for computing the router loss,
-                and should not be returned during inference.
-            use_cache (`bool`, *optional*):
-                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
-                (see `past_key_values`).
-            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
-            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
-                Indices depicting the position of the input sequence tokens in the sequence.
-        """
-
-        residual = hidden_states
-
-        hidden_states = self.input_layernorm(hidden_states)
-
-        # Self Attention
-        hidden_states, self_attn_weights, present_key_value = self.self_attn(
-            hidden_states=hidden_states,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_value=past_key_value,
-            output_attentions=output_attentions,
-            use_cache=use_cache,
-            cache_position=cache_position,
-        )
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-
-        hidden_states = self.mlp(hidden_states,language_ids)
-        if isinstance(hidden_states, tuple):
-            hidden_states, router_logits = hidden_states
-        else:
-            router_logits = None
-
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        if output_router_logits:
-            outputs += (router_logits,)
-
-        return outputs
-
-
-class UpcyclingQwen2MoePreTrainedModel(PreTrainedModel):
-    config_class = UpcyclingQwen2MoeConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["Qwen2MoeDecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = True
-    _supports_sdpa = True
-    _supports_cache_class = True
-
-    def _init_weights(self, module):
-        std = self.config.initializer_range
-        if isinstance(module, nn.Linear):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.Embedding):
-            module.weight.data.normal_(mean=0.0, std=std)
-            if module.padding_idx is not None:
-                module.weight.data[module.padding_idx].zero_()
-
-    @classmethod
-    def from_qwen(cls, pretrained_model_name_or_path, *model_args, **kwargs):
-        share_flag=kwargs.pop('share_flag')
-        attn_init_change=kwargs.pop('attn_init_change')
-        language_gate=kwargs.pop('language_gate')
-
-        config = cls.config_class.from_pretrained(pretrained_model_name_or_path)
-
-        config.share_flag=True if isinstance(share_flag,bool) and share_flag else False
-        config.attn_init_change=True if isinstance(attn_init_change,bool) and attn_init_change else False
-        config.language_gate=True if isinstance(language_gate,bool) and language_gate else False
-
-        print('share_flag',config.share_flag)
-        print('attn_init_change',config.attn_init_change)
-        print('language_gate',config.language_gate)
-
-        config.num_experts_per_tok = config.num_experts_per_tok if not config.share_flag else config.num_experts_per_tok-1
-        config.num_experts = config.num_experts if not config.share_flag else config.num_experts-1
-
-        base_model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
-        base_cls = type(base_model)
-
-        print(cls.config_class,cls)
-  
-        #create auto_map
-        #allows you to use your custom model with the auto-API (but doesn’t share any custom code with other users).
-        cls.config_class.register_for_auto_class()
-        cls.register_for_auto_class('AutoModelForCausalLM')
-
-        # assert base_cls.__name__ == "Qwen2ForCausalLM", f"Invalid convert base model type: {base_cls}"
-
-        model = cls(config)
-        print(f"converting {base_cls.__name__} to {cls.__name__}")
-
-        #MoE architechture
-        model_dict=model.state_dict()
-        base_model_dict = base_model.state_dict()
-
-        #lm_head
-        print('lm_head.weight',model_dict['lm_head.weight'],base_model_dict['lm_head.weight'])
-
-        shared_keys=set(model_dict)&set(base_model_dict)
-        init_keys=[]
-        #attention
-        for k in shared_keys:
-            if k not in init_keys and 'self_attn' in k:
-                init_keys.append(k)
-                if not config.attn_init_change:
-                    model_dict[k]=base_model_dict[k]
-
-        if config.attn_init_change:
-            #initilization with upper and lower
-            for layer_id in range(config.num_hidden_layers):
-                if layer_id ==0 or config.num_hidden_layers-1:
-                    model_dict[f'model.layers.{layer_id}.self_attn.q_proj.bias']=base_model_dict[f'model.layers.{layer_id}.self_attn.q_proj.bias']
-                    model_dict[f'model.layers.{layer_id}.self_attn.q_proj.weight']=base_model_dict[f'model.layers.{layer_id}.self_attn.q_proj.weight']
-                    model_dict[f'model.layers.{layer_id}.self_attn.k_proj.bias']=base_model_dict[f'model.layers.{layer_id}.self_attn.k_proj.bias']
-                    model_dict[f'model.layers.{layer_id}.self_attn.k_proj.weight']=base_model_dict[f'model.layers.{layer_id}.self_attn.k_proj.weight']
-                    model_dict[f'model.layers.{layer_id}.self_attn.v_proj.bias']=base_model_dict[f'model.layers.{layer_id}.self_attn.v_proj.bias']
-                    model_dict[f'model.layers.{layer_id}.self_attn.v_proj.weight']=base_model_dict[f'model.layers.{layer_id}.self_attn.v_proj.weight']
-                    model_dict[f'model.layers.{layer_id}.self_attn.o_proj.weight']=base_model_dict[f'model.layers.{layer_id}.self_attn.o_proj.weight']
-                else:
-                    model_dict[f'model.layers.{layer_id}.self_attn.q_proj.bias']=1/3*(base_model_dict[f'model.layers.{layer_id}.self_attn.q_proj.bias']+base_model_dict[f'model.layers.{layer_id+1}.self_attn.q_proj.bias']+base_model_dict[f'model.layers.{layer_id-1}.self_attn.q_proj.bias'])
-                    model_dict[f'model.layers.{layer_id}.self_attn.q_proj.weight']=1/3*(base_model_dict[f'model.layers.{layer_id}.self_attn.q_proj.weight']+base_model_dict[f'model.layers.{layer_id+1}.self_attn.q_proj.weight']+base_model_dict[f'model.layers.{layer_id-1}.self_attn.q_proj.weight'])
-                    model_dict[f'model.layers.{layer_id}.self_attn.k_proj.bias']=1/3*(base_model_dict[f'model.layers.{layer_id}.self_attn.k_proj.bias']+base_model_dict[f'model.layers.{layer_id+1}.self_attn.k_proj.bias']+base_model_dict[f'model.layers.{layer_id-1}.self_attn.k_proj.bias'])
-                    model_dict[f'model.layers.{layer_id}.self_attn.k_proj.weight']=1/3*(base_model_dict[f'model.layers.{layer_id}.self_attn.k_proj.weight']+base_model_dict[f'model.layers.{layer_id+1}.self_attn.k_proj.weight']+base_model_dict[f'model.layers.{layer_id-1}.self_attn.k_proj.weight'])
-                    model_dict[f'model.layers.{layer_id}.self_attn.v_proj.bias']=1/3*(base_model_dict[f'model.layers.{layer_id}.self_attn.v_proj.bias']+base_model_dict[f'model.layers.{layer_id+1}.self_attn.v_proj.bias']+base_model_dict[f'model.layers.{layer_id-1}.self_attn.v_proj.bias'])
-                    model_dict[f'model.layers.{layer_id}.self_attn.v_proj.weight']=1/3*(base_model_dict[f'model.layers.{layer_id}.self_attn.v_proj.weight']+base_model_dict[f'model.layers.{layer_id+1}.self_attn.v_proj.weight']+base_model_dict[f'model.layers.{layer_id-1}.self_attn.v_proj.weight'])
-                    model_dict[f'model.layers.{layer_id}.self_attn.o_proj.weight']=1/3*(base_model_dict[f'model.layers.{layer_id}.self_attn.o_proj.weight']+base_model_dict[f'model.layers.{layer_id+1}.self_attn.o_proj.weight']+base_model_dict[f'model.layers.{layer_id-1}.self_attn.o_proj.weight'])
-        
-        #mlp
-        if config.mlp_only_layers:
-            for layer_id in config.mlp_only_layers:
-                key_mapping=sum([
-                    [
-                        (f'model.layers.{layer_id}.mlp.down_proj.weight',f'model.layers.{layer_id}.mlp.down_proj.weight'),
-                        (f'model.layers.{layer_id}.mlp.gate_proj.weight',f'model.layers.{layer_id}.mlp.gate_proj.weight'),
-                        (f'model.layers.{layer_id}.mlp.up_proj.weight',f'model.layers.{layer_id}.mlp.up_proj.weight'),
-                    ]]
-                ,[])
-                for model_key,base_model_key in key_mapping:
-                    model_dict[model_key]=base_model_dict[base_model_key]
-                    init_keys.append(model_key)
-        moe_only_layers=list(set(range(config.num_hidden_layers))-set(config.mlp_only_layers)) if config.mlp_only_layers else config.num_hidden_layers
-        #moe-mlp-expert
-        for layer_id in moe_only_layers:
-            key_mapping=sum([
-                [
-                    (f'model.layers.{layer_id}.mlp.experts.{expert_id}.down_proj.weight',f'model.layers.{layer_id}.mlp.down_proj.weight'),
-                    (f'model.layers.{layer_id}.mlp.experts.{expert_id}.gate_proj.weight',f'model.layers.{layer_id}.mlp.gate_proj.weight'),
-                    (f'model.layers.{layer_id}.mlp.experts.{expert_id}.up_proj.weight',f'model.layers.{layer_id}.mlp.up_proj.weight'),
-                ] for expert_id in range(config.num_experts)]
-            ,[])
-            for model_key,base_model_key in key_mapping:
-                model_dict[model_key]=base_model_dict[base_model_key]
-                init_keys.append(model_key)
-            #model_dict[f'model.layers.{layer_id}.mlp.gate.weight']
-
-        #share expert
-        if config.share_flag:
-            shared_key_mapping=sum([[
-                (f'model.layers.{layer_id}.mlp.shared_expert.down_proj.weight',f'model.layers.{layer_id}.mlp.down_proj.weight'),
-                (f'model.layers.{layer_id}.mlp.shared_expert.gate_proj.weight',f'model.layers.{layer_id}.mlp.gate_proj.weight'),
-                (f'model.layers.{layer_id}.mlp.shared_expert.up_proj.weight',f'model.layers.{layer_id}.mlp.up_proj.weight'),
-            ]for layer_id in range(config.num_hidden_layers)],
-            [])
-            for model_key,base_model_key in shared_key_mapping:
-                model_dict[model_key]=base_model_dict[base_model_key]
-                init_keys.append(model_key)
-            # model_dict[f'model.layers.{layer_id}.mlp.shared_expert_gate.weight']
-
-        #norm
-        for k in shared_keys:
-            if k not in init_keys:
-                #input_layernorm.weight,post_attention_layernorm.weight,norm.weight
-                # embed_token.weight,lm_head.weight
-                model_dict[k]=base_model_dict[k]
-                init_keys.append(k)
-        
-        gate_initialized = False
-        shared_gate_initilizaed=False
-        for key in model_dict.keys():
-            if key in init_keys:
-                continue
-            if "mlp.gate.weight" in key:
-                if gate_initialized:
-                    continue
-                gate_initialized = True
-                print(f"{cls.__name__} key [{cls.base_model_prefix}.layers.[0-{config.num_hidden_layers-1}].mlp.gate.weight] is not initialized from {base_cls.__name__}. e.g, {key}")
-                continue
-            if 'shared_expert_gate.weight' in key:
-                if shared_gate_initilizaed:
-                    continue
-                shared_gate_initilizaed = True
-                print(f"{cls.__name__} key [{cls.base_model_prefix}.layers.[0-{config.num_hidden_layers-1}].mlp.shared_expert_gate.weight] is not initialized from {base_cls.__name__}. e.g, {key}")
-                continue
-
-            raise NotImplementedError(f"{cls.__name__} key [{key}] is not correctly initilized from {base_cls.__name__}.")
-
-        model.load_state_dict(model_dict)
-        print(f"Done converted, alreadly check all parameters of {cls.__name__} are initialized from {base_cls.__name__}.")
-
-        del base_model
-        return model
-    
-    @classmethod
-    def from_btx(cls, pretrained_model_name_or_path, *model_args, **kwargs):
-        share_flag=kwargs.pop('share_flag')
-        attn_init_change=kwargs.pop('attn_init_change')
-        language_gate=kwargs.pop('language_gate')
-
-        config = cls.config_class.from_pretrained(pretrained_model_name_or_path)
-
-        config.share_flag=True if isinstance(share_flag,bool) and share_flag else False
-        config.attn_init_change=True if isinstance(attn_init_change,bool) and attn_init_change else False
-        config.language_gate=True if isinstance(language_gate,bool) and language_gate else False
-
-        print('share_flag',config.share_flag)
-        print('attn_init_change',config.attn_init_change)
-        print('language_gate',config.language_gate)
-
-        config.num_experts_per_tok = config.num_experts_per_tok if not config.share_flag else config.num_experts_per_tok-1
-        config.num_experts = config.num_experts if not config.share_flag else config.num_experts-1
-
-        base_model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
-        base_cls = type(base_model)
-
-        print(cls.config_class,cls)
-  
-        #create auto_map
-        #allows you to use your custom model with the auto-API (but doesn’t share any custom code with other users).
-        cls.config_class.register_for_auto_class()
-        cls.register_for_auto_class('AutoModelForCausalLM')
-
-        # assert base_cls.__name__ == "Qwen2ForCausalLM", f"Invalid convert base model type: {base_cls}"
-
-        model = cls(config)
-        print(f"converting {base_cls.__name__} to {cls.__name__}")
-
-        #MoE architechture
-        model_dict=model.state_dict()
-        base_model_dict = base_model.state_dict()
-
-        #lm_head
-        print('lm_head.weight',model_dict['lm_head.weight'],base_model_dict['lm_head.weight'])
-
-        shared_keys=set(model_dict)&set(base_model_dict)
-        init_keys=[]
-        #attention
-        for k in shared_keys:
-            init_keys.append(k)
-            model_dict[k]=base_model_dict[k]
-
-        gate_initialized = False
-        shared_gate_initilizaed=False
-        for key in model_dict.keys():
-            if key in init_keys:
-                continue
-            if "mlp.gate.weight" in key:
-                if gate_initialized:
-                    continue
-                gate_initialized = True
-                print(f"{cls.__name__} key [{cls.base_model_prefix}.layers.[0-{config.num_hidden_layers-1}].mlp.gate.weight] is not initialized from {base_cls.__name__}. e.g, {key}")
-                continue
-            if 'shared_expert_gate.weight' in key:
-                if shared_gate_initilizaed:
-                    continue
-                shared_gate_initilizaed = True
-                print(f"{cls.__name__} key [{cls.base_model_prefix}.layers.[0-{config.num_hidden_layers-1}].mlp.shared_expert_gate.weight] is not initialized from {base_cls.__name__}. e.g, {key}")
-                continue
-
-            raise NotImplementedError(f"{cls.__name__} key [{key}] is not correctly initilized from {base_cls.__name__}.")
-
-        model.load_state_dict(model_dict)
-        print(f"Done converted, alreadly check all parameters of {cls.__name__} are initialized from {base_cls.__name__}.")
-
-        del base_model
-        return model
-
-class UpcyclingQwen2MoeModel(UpcyclingQwen2MoePreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2MoeDecoderLayer`]
-
-    Args:
-        config: Qwen2MoeConfig
-    """
-
-    def __init__(self, config: UpcyclingQwen2MoeConfig):
-        super().__init__(config)
-        self.padding_idx = config.pad_token_id
-        self.vocab_size = config.vocab_size
-
-        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.layers = nn.ModuleList(
-            [Qwen2MoeDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
-        )
-        self._attn_implementation = config._attn_implementation
-        self.norm = Qwen2MoeRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-
-        self.gradient_checkpointing = False
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.embed_tokens = value
-
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        language_ids :Optional[torch.LongTensor]= None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_router_logits: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple, MoeModelOutputWithPast]:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_router_logits = (
-            output_router_logits if output_router_logits is not None else self.config.output_router_logits
-        )
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        if (input_ids is None) ^ (inputs_embeds is not None):
-            raise ValueError(
-                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
-            )
-
-        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
-
-        use_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):
-            use_legacy_cache = True
-            past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            logger.warning_once(
-                "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. "
-                "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)"
-            )
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        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)
-
-        causal_mask = self._update_causal_mask(
-            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
-        )
-
-        hidden_states = inputs_embeds
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions else None
-        all_router_logits = () if output_router_logits else None
-        next_decoder_cache = None
-
-        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(
-                    decoder_layer.__call__,
-                    hidden_states,
-                    language_ids,
-                    causal_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    output_router_logits,
-                    use_cache,
-                    cache_position,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    language_ids,
-                    attention_mask=causal_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    output_router_logits=output_router_logits,
-                    use_cache=use_cache,
-                    cache_position=cache_position,
-                )
-
-            hidden_states = layer_outputs[0]
-
-            if use_cache:
-                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
-
-            if output_attentions:
-                all_self_attns += (layer_outputs[1],)
-
-            if output_router_logits and layer_outputs[-1] is not None:
-                all_router_logits += (layer_outputs[-1],)
-
-        hidden_states = self.norm(hidden_states)
-
-        # add hidden states from the last decoder layer
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        next_cache = None
-        if use_cache:
-            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
-
-        if not return_dict:
-            return tuple(
-                v
-                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits]
-                if v is not None
-            )
-        return MoeModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-            router_logits=all_router_logits,
-        )
-
-    # Copied from transformers.models.llama.modeling_llama.LlamaModel._update_causal_mask
-    def _update_causal_mask(
-        self,
-        attention_mask: torch.Tensor,
-        input_tensor: torch.Tensor,
-        cache_position: torch.Tensor,
-        past_key_values: Cache,
-        output_attentions: bool,
-    ):
-        # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static
-        # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes.
-        # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using
-        # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114
-
-        if self.config._attn_implementation == "flash_attention_2":
-            if attention_mask is not None and 0.0 in attention_mask:
-                return attention_mask
-            return None
-
-        # 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, device = input_tensor.dtype, input_tensor.device
-        min_dtype = torch.finfo(dtype).min
-        sequence_length = input_tensor.shape[1]
-        if using_static_cache:
-            target_length = past_key_values.get_max_length()
-        else:
-            target_length = (
-                attention_mask.shape[-1]
-                if isinstance(attention_mask, torch.Tensor)
-                else past_seen_tokens + sequence_length + 1
-            )
-
-        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
-            if attention_mask.max() != 0:
-                raise ValueError("Custom 4D attention mask should be passed in inverted form with max==0`")
-            causal_mask = attention_mask
-        else:
-            causal_mask = torch.full(
-                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
-            )
-            if sequence_length != 1:
-                causal_mask = torch.triu(causal_mask, diagonal=1)
-            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
-            causal_mask = causal_mask[None, None, :, :].expand(input_tensor.shape[0], 1, -1, -1)
-            if attention_mask is not None:
-                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
-                mask_length = attention_mask.shape[-1]
-                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
-                padding_mask = padding_mask == 0
-                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
-                    padding_mask, min_dtype
-                )
-        if (
-            self.config._attn_implementation == "sdpa"
-            and attention_mask is not None
-            and attention_mask.device.type == "cuda"
-            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
-
-
-class UpcyclingQwen2MoeForCausalLM(UpcyclingQwen2MoePreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = UpcyclingQwen2MoeModel(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        self.router_aux_loss_coef = config.router_aux_loss_coef
-        self.num_experts = config.num_experts
-        self.num_experts_per_tok = config.num_experts_per_tok
-        
-        self.language_gate=config.language_gate
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.embed_tokens
-
-    def set_input_embeddings(self, value):
-        self.model.embed_tokens = value
-
-    def get_output_embeddings(self):
-        return self.lm_head
-
-    def set_output_embeddings(self, new_embeddings):
-        self.lm_head = new_embeddings
-
-    def set_decoder(self, decoder):
-        self.model = decoder
-
-    def get_decoder(self):
-        return self.model
-
-    # @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
-    def forward(
-        self,
-        input_ids: torch.LongTensor = None,
-        language_ids: Optional[torch.LongTensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_values: Optional[List[torch.FloatTensor]] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-        labels: Optional[torch.LongTensor] = None,
-        use_cache: Optional[bool] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        output_router_logits: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-        cache_position: Optional[torch.LongTensor] = None,
-    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
-        
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_router_logits = (
-            output_router_logits if output_router_logits is not None else self.config.output_router_logits
-        )
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
-
-        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
-        outputs = self.model(
-            input_ids=input_ids,
-            language_ids=language_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            output_router_logits=output_router_logits,
-            return_dict=return_dict,
-            cache_position=cache_position,
-        )
-
-        hidden_states = outputs[0]
-        logits = self.lm_head(hidden_states)
-        logits = logits.float()
-
-        loss = None
-        if labels is not None:
-            # Shift so that tokens < n predict n
-            shift_logits = logits[..., :-1, :].contiguous()
-            shift_labels = labels[..., 1:].contiguous()
-            # Flatten the tokens
-            loss_fct = CrossEntropyLoss()
-            shift_logits = shift_logits.view(-1, self.config.vocab_size)
-            shift_labels = shift_labels.view(-1)
-            # Enable model parallelism
-            shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
-
-        aux_loss = None
-        if output_router_logits:
-            aux_loss = load_balancing_loss_func(
-                outputs.router_logits if return_dict else outputs[-1],
-                self.num_experts,
-                self.num_experts_per_tok,
-                attention_mask,
-            )
-            if labels is not None:
-                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            if output_router_logits:
-                output = (aux_loss,) + output
-            return (loss,) + output if loss is not None else output
-
-        return MoeCausalLMOutputWithPast(
-            loss=loss,
-            aux_loss=aux_loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-            router_logits=outputs.router_logits,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        cache_position=None,
-        use_cache=True,
-        **kwargs,
-    ):
-        past_length = 0
-
-# ##### by own
-        if past_key_values is not None:
-            if isinstance(past_key_values,Cache):
-            # Past key values are always initialized with a `Cache` object -> no need for if-else anymore
-                past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
-                max_cache_length = (
-                    torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
-                    if past_key_values.get_max_length() is not None
-                    else None
-                )
-                cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
-            else:
-                cache_length=past_length=past_key_values[0][0].shape[2]
-                max_cache_length=None
-# # #####
-        # Omit tokens covered by past_key_values
-        # if past_key_values is not None:
-        #     # Past key values are always initialized with a `Cache` object -> no need for if-else anymore
-        #     past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length()
-        #     max_cache_length = (
-        #         torch.tensor(past_key_values.get_max_length(), device=input_ids.device)
-        #         if past_key_values.get_max_length() is not None
-        #         else None
-        #     )
-        #     cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length)
-
-            # Keep only the unprocessed tokens:
-            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
-            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
-            # input)
-            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
-                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
-            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
-            # input_ids based on the past_length.
-            elif past_length < input_ids.shape[1]:
-                input_ids = input_ids[:, past_length:]
-            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
-
-            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
-            if (
-                max_cache_length is not None
-                and attention_mask is not None
-                and cache_length + input_ids.shape[1] > max_cache_length
-            ):
-                attention_mask = attention_mask[:, -max_cache_length:]
-
-        position_ids = kwargs.get("position_ids", None)
-        if attention_mask is not None and position_ids is None:
-            # create position_ids on the fly for batch generation
-            position_ids = attention_mask.long().cumsum(-1) - 1
-            position_ids.masked_fill_(attention_mask == 0, 1)
-            if past_key_values:
-                position_ids = position_ids[:, -input_ids.shape[1] :]
-
-        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
-        if inputs_embeds is not None and past_length == 0:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-
-        input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1]
-        if cache_position is None:
-            cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device)
-        elif use_cache:
-            cache_position = cache_position[-input_length:]
-
-        model_inputs.update(
-            {
-                "position_ids": position_ids,
-                "past_key_values": past_key_values,
-                "use_cache": use_cache,
-                "attention_mask": attention_mask,
-                "cache_position": cache_position,
-            }
-        )
-        return model_inputs
-
-    @staticmethod
-    def _reorder_cache(past_key_values, beam_idx):
-        reordered_past = ()
-        for layer_past in past_key_values:
-            reordered_past += (
-                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
-            )
-        return reordered_past
-
-