Delete modeling_deepseek.py

modeling_deepseek.py

@@ -1,1916 +0,0 @@
-# coding=utf-8
-# Copyright 2023 DeepSeek-AI 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 DeepSeek model."""
-import math
-import warnings
-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
-from transformers.modeling_attn_mask_utils import (
-    AttentionMaskConverter,
-    _prepare_4d_attention_mask,
-    _prepare_4d_causal_attention_mask,
-)
-from transformers.modeling_outputs import (
-    BaseModelOutputWithPast,
-    CausalLMOutputWithPast,
-    SequenceClassifierOutputWithPast,
-)
-from transformers.modeling_utils import PreTrainedModel
-from transformers.pytorch_utils import (
-    ALL_LAYERNORM_LAYERS,
-    is_torch_greater_or_equal_than_1_13,
-)
-from transformers.utils import (
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    is_flash_attn_2_available,
-    is_flash_attn_greater_or_equal_2_10,
-    logging,
-    replace_return_docstrings,
-)
-from transformers.utils.import_utils import is_torch_fx_available
-from .configuration_deepseek import DeepseekV2Config
-import torch.distributed as dist
-import numpy as np
-
-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
-
-
-# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
-# It means that the function will not be traced through and simply appear as a node in the graph.
-if is_torch_fx_available():
-    if not is_torch_greater_or_equal_than_1_13:
-        import torch.fx
-
-    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
-
-
-logger = logging.get_logger(__name__)
-
-_CONFIG_FOR_DOC = "DeepseekV2Config"
-
-
-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.torch.int32), (1, 0)
-    )
-    return (
-        indices,
-        cu_seqlens,
-        max_seqlen_in_batch,
-    )
-
-
-class DeepseekV2RMSNorm(nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
-        """
-        DeepseekV2RMSNorm 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)
-
-
-ALL_LAYERNORM_LAYERS.append(DeepseekV2RMSNorm)
-
-
-class DeepseekV2RotaryEmbedding(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).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(),
-        )
-        self.max_seq_len_cached = None
-
-    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=self.inv_freq.dtype
-        )
-
-        freqs = torch.outer(t, self.inv_freq.to(t.device))
-        # 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 self.max_seq_len_cached is None or 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.LlamaLinearScalingRotaryEmbedding with Llama->DeepseekV2
-class DeepseekV2LinearScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
-    """DeepseekV2RotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
-
-    def __init__(
-        self,
-        dim,
-        max_position_embeddings=2048,
-        base=10000,
-        device=None,
-        scaling_factor=1.0,
-    ):
-        self.scaling_factor = scaling_factor
-        super().__init__(dim, max_position_embeddings, base, device)
-
-    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=self.inv_freq.dtype
-        )
-        t = t / self.scaling_factor
-
-        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)
-
-
-# Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->DeepseekV2
-class DeepseekV2DynamicNTKScalingRotaryEmbedding(DeepseekV2RotaryEmbedding):
-    """DeepseekV2RotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
-
-    def __init__(
-        self,
-        dim,
-        max_position_embeddings=2048,
-        base=10000,
-        device=None,
-        scaling_factor=1.0,
-    ):
-        self.scaling_factor = scaling_factor
-        super().__init__(dim, max_position_embeddings, base, device)
-
-    def _set_cos_sin_cache(self, seq_len, device, dtype):
-        self.max_seq_len_cached = seq_len
-
-        if seq_len > self.max_position_embeddings:
-            base = self.base * (
-                (self.scaling_factor * seq_len / self.max_position_embeddings)
-                - (self.scaling_factor - 1)
-            ) ** (self.dim / (self.dim - 2))
-            inv_freq = 1.0 / (
-                base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
-            )
-            self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        t = torch.arange(
-            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
-        )
-
-        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)
-
-
-# Inverse dim formula to find dim based on number of rotations
-def yarn_find_correction_dim(
-    num_rotations, dim, base=10000, max_position_embeddings=2048
-):
-    return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
-        2 * math.log(base)
-    )
-
-
-# Find dim range bounds based on rotations
-def yarn_find_correction_range(
-    low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
-):
-    low = math.floor(
-        yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)
-    )
-    high = math.ceil(
-        yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)
-    )
-    return max(low, 0), min(high, dim - 1)  # Clamp values just in case
-
-
-def yarn_get_mscale(scale=1, mscale=1):
-    if scale <= 1:
-        return 1.0
-    return 0.1 * mscale * math.log(scale) + 1.0
-
-
-def yarn_linear_ramp_mask(min, max, dim):
-    if min == max:
-        max += 0.001  # Prevent singularity
-
-    linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
-    ramp_func = torch.clamp(linear_func, 0, 1)
-    return ramp_func
-
-
-class DeepseekV2YarnRotaryEmbedding(DeepseekV2RotaryEmbedding):
-
-    def __init__(
-        self,
-        dim,
-        max_position_embeddings=2048,
-        base=10000,
-        device=None,
-        scaling_factor=1.0,
-        original_max_position_embeddings=4096,
-        beta_fast=32,
-        beta_slow=1,
-        mscale=1,
-        mscale_all_dim=0,
-    ):
-        self.scaling_factor = scaling_factor
-        self.original_max_position_embeddings = original_max_position_embeddings
-        self.beta_fast = beta_fast
-        self.beta_slow = beta_slow
-        self.mscale = mscale
-        self.mscale_all_dim = mscale_all_dim
-        super().__init__(dim, max_position_embeddings, base, device)
-
-    def _set_cos_sin_cache(self, seq_len, device, dtype):
-        self.max_seq_len_cached = seq_len
-        dim = self.dim
-
-        freq_extra = 1.0 / (
-            self.base
-            ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
-        )
-        freq_inter = 1.0 / (
-            self.scaling_factor
-            * self.base
-            ** (torch.arange(0, dim, 2, dtype=torch.float32, device=device) / dim)
-        )
-
-        low, high = yarn_find_correction_range(
-            self.beta_fast,
-            self.beta_slow,
-            dim,
-            self.base,
-            self.original_max_position_embeddings,
-        )
-        inv_freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2).to(
-            device=device, dtype=torch.float32
-        )
-        inv_freq = freq_inter * (1 - inv_freq_mask) + freq_extra * inv_freq_mask
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        t = torch.arange(seq_len, device=device, dtype=torch.float32)
-
-        freqs = torch.outer(t, inv_freq)
-
-        _mscale = float(
-            yarn_get_mscale(self.scaling_factor, self.mscale)
-            / yarn_get_mscale(self.scaling_factor, self.mscale_all_dim)
-        )
-
-        emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer(
-            "cos_cached", (emb.cos() * _mscale).to(dtype), persistent=False
-        )
-        self.register_buffer(
-            "sin_cached", (emb.sin() * _mscale).to(dtype), persistent=False
-        )
-
-
-# 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.llama.modeling_llama.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)
-
-    b, h, s, d = q.shape
-    q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
-
-    b, h, s, d = k.shape
-    k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
-
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
-    return q_embed, k_embed
-
-
-class DeepseekV2MLP(nn.Module):
-    def __init__(self, config, hidden_size=None, intermediate_size=None):
-        super().__init__()
-        self.config = config
-        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
-        self.intermediate_size = (
-            config.intermediate_size if intermediate_size is None else intermediate_size
-        )
-
-        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
-        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
-        self.act_fn = ACT2FN[config.hidden_act]
-
-    def forward(self, x):
-        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
-        return down_proj
-
-
-class MoEGate(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.top_k = config.num_experts_per_tok
-        self.n_routed_experts = config.n_routed_experts
-        self.routed_scaling_factor = config.routed_scaling_factor
-        self.scoring_func = config.scoring_func
-        self.alpha = config.aux_loss_alpha
-        self.seq_aux = config.seq_aux
-        self.topk_method = config.topk_method
-        self.n_group = config.n_group
-        self.topk_group = config.topk_group
-
-        # topk selection algorithm
-        self.norm_topk_prob = config.norm_topk_prob
-        self.gating_dim = config.hidden_size
-        self.weight = nn.Parameter(
-            torch.empty((self.n_routed_experts, self.gating_dim))
-        )
-        self.reset_parameters()
-
-    def reset_parameters(self) -> None:
-        import torch.nn.init as init
-
-        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
-
-    def forward(self, hidden_states):
-        bsz, seq_len, h = hidden_states.shape
-        ### compute gating score
-        hidden_states = hidden_states.view(-1, h)
-        logits = F.linear(
-            hidden_states.type(torch.float32), self.weight.type(torch.float32), None
-        )
-        if self.scoring_func == "softmax":
-            scores = logits.softmax(dim=-1, dtype=torch.float32)
-        else:
-            raise NotImplementedError(
-                f"insupportable scoring function for MoE gating: {self.scoring_func}"
-            )
-
-        ### select top-k experts
-        if self.topk_method == "greedy":
-            topk_weight, topk_idx = torch.topk(
-                scores, k=self.top_k, dim=-1, sorted=False
-            )
-        elif self.topk_method == "group_limited_greedy":
-            group_scores = (
-                scores.view(bsz * seq_len, self.n_group, -1).max(dim=-1).values
-            )  # [n, n_group]
-            group_idx = torch.topk(
-                group_scores, k=self.topk_group, dim=-1, sorted=False
-            )[
-                1
-            ]  # [n, top_k_group]
-            group_mask = torch.zeros_like(group_scores)  # [n, n_group]
-            group_mask.scatter_(1, group_idx, 1)  # [n, n_group]
-            score_mask = (
-                group_mask.unsqueeze(-1)
-                .expand(
-                    bsz * seq_len, self.n_group, self.n_routed_experts // self.n_group
-                )
-                .reshape(bsz * seq_len, -1)
-            )  # [n, e]
-            tmp_scores = scores.masked_fill(~score_mask.bool(), 0.0)  # [n, e]
-            topk_weight, topk_idx = torch.topk(
-                tmp_scores, k=self.top_k, dim=-1, sorted=False
-            )
-
-        ### norm gate to sum 1
-        if self.top_k > 1 and self.norm_topk_prob:
-            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
-            topk_weight = topk_weight / denominator
-        else:
-            topk_weight = topk_weight * self.routed_scaling_factor
-        ### expert-level computation auxiliary loss
-        if self.training and self.alpha > 0.0:
-            scores_for_aux = scores
-            aux_topk = self.top_k
-            # always compute aux loss based on the naive greedy topk method
-            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
-            if self.seq_aux:
-                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
-                ce = torch.zeros(
-                    bsz, self.n_routed_experts, device=hidden_states.device
-                )
-                ce.scatter_add_(
-                    1,
-                    topk_idx_for_aux_loss,
-                    torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device),
-                ).div_(seq_len * aux_topk / self.n_routed_experts)
-                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(
-                    dim=1
-                ).mean() * self.alpha
-            else:
-                mask_ce = F.one_hot(
-                    topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts
-                )
-                ce = mask_ce.float().mean(0)
-                Pi = scores_for_aux.mean(0)
-                fi = ce * self.n_routed_experts
-                aux_loss = (Pi * fi).sum() * self.alpha
-        else:
-            aux_loss = None
-        return topk_idx, topk_weight, aux_loss
-
-
-class AddAuxiliaryLoss(torch.autograd.Function):
-    """
-    The trick function of adding auxiliary (aux) loss,
-    which includes the gradient of the aux loss during backpropagation.
-    """
-
-    @staticmethod
-    def forward(ctx, x, loss):
-        assert loss.numel() == 1
-        ctx.dtype = loss.dtype
-        ctx.required_aux_loss = loss.requires_grad
-        return x
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        grad_loss = None
-        if ctx.required_aux_loss:
-            grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device)
-        return grad_output, grad_loss
-
-
-class DeepseekV2MoE(nn.Module):
-    """
-    A mixed expert module containing shared experts.
-    """
-
-    def __init__(self, config):
-        super().__init__()
-        self.config = config
-        self.num_experts_per_tok = config.num_experts_per_tok
-
-        if hasattr(config, "ep_size") and config.ep_size > 1:
-            assert config.ep_size == dist.get_world_size()
-            self.ep_size = config.ep_size
-            self.experts_per_rank = config.n_routed_experts // config.ep_size
-            self.ep_rank = dist.get_rank()
-            self.experts = nn.ModuleList(
-                [
-                    (
-                        DeepseekV2MLP(
-                            config, intermediate_size=config.moe_intermediate_size
-                        )
-                        if i >= self.ep_rank * self.experts_per_rank
-                        and i < (self.ep_rank + 1) * self.experts_per_rank
-                        else None
-                    )
-                    for i in range(config.n_routed_experts)
-                ]
-            )
-        else:
-            self.ep_size = 1
-            self.experts_per_rank = config.n_routed_experts
-            self.ep_rank = 0
-            self.experts = nn.ModuleList(
-                [
-                    DeepseekV2MLP(config, intermediate_size=config.moe_intermediate_size)
-                    for i in range(config.n_routed_experts)
-                ]
-            )
-        self.gate = MoEGate(config)
-        if config.n_shared_experts is not None:
-            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
-            self.shared_experts = DeepseekV2MLP(
-                config=config, intermediate_size=intermediate_size
-            )
-
-    def forward(self, hidden_states):
-        identity = hidden_states
-        orig_shape = hidden_states.shape
-        topk_idx, topk_weight, aux_loss = self.gate(hidden_states)
-        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
-        flat_topk_idx = topk_idx.view(-1)
-        if self.training:
-            hidden_states = hidden_states.repeat_interleave(
-                self.num_experts_per_tok, dim=0
-            )
-            y = torch.empty_like(hidden_states)
-            for i, expert in enumerate(self.experts):
-                y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i])
-            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
-            y = y.view(*orig_shape)
-            y = AddAuxiliaryLoss.apply(y, aux_loss)
-        else:
-            y = self.moe_infer(hidden_states, topk_idx, topk_weight).view(*orig_shape)
-        if self.config.n_shared_experts is not None:
-            y = y + self.shared_experts(identity)
-        return y
-
-    @torch.no_grad()
-    def moe_infer(self, x, topk_ids, topk_weight):
-        cnts = topk_ids.new_zeros((topk_ids.shape[0], len(self.experts)))
-        cnts.scatter_(1, topk_ids, 1)
-        tokens_per_expert = cnts.sum(dim=0)
-        idxs = topk_ids.view(-1).argsort()
-        sorted_tokens = x[idxs // topk_ids.shape[1]]
-        sorted_tokens_shape = sorted_tokens.shape
-        if self.ep_size > 1:
-            tokens_per_ep_rank = tokens_per_expert.view(self.ep_size, -1).sum(dim=1)
-            tokens_per_expert_group = tokens_per_expert.new_empty(
-                tokens_per_expert.shape[0]
-            )
-            dist.all_to_all_single(tokens_per_expert_group, tokens_per_expert)
-            output_splits = (
-                tokens_per_expert_group.view(self.ep_size, -1)
-                .sum(1)
-                .cpu()
-                .numpy()
-                .tolist()
-            )
-            gathered_tokens = sorted_tokens.new_empty(
-                tokens_per_expert_group.sum(dim=0).cpu().item(), sorted_tokens.shape[1]
-            )
-            input_split_sizes = tokens_per_ep_rank.cpu().numpy().tolist()
-            dist.all_to_all(
-                list(gathered_tokens.split(output_splits)),
-                list(sorted_tokens.split(input_split_sizes)),
-            )
-            tokens_per_expert_post_gather = tokens_per_expert_group.view(
-                self.ep_size, self.experts_per_rank
-            ).sum(dim=0)
-            gatherd_idxs = np.zeros(shape=(gathered_tokens.shape[0],), dtype=np.int32)
-            s = 0
-            for i, k in enumerate(tokens_per_expert_group.cpu().numpy()):
-                gatherd_idxs[s : s + k] = i % self.experts_per_rank
-                s += k
-            gatherd_idxs = gatherd_idxs.argsort()
-            sorted_tokens = gathered_tokens[gatherd_idxs]
-            tokens_per_expert = tokens_per_expert_post_gather
-        tokens_per_expert = tokens_per_expert.cpu().numpy()
-
-        outputs = []
-        start_idx = 0
-        for i, num_tokens in enumerate(tokens_per_expert):
-            end_idx = start_idx + num_tokens
-            if num_tokens == 0:
-                continue
-            expert = self.experts[i + self.ep_rank * self.experts_per_rank]
-            tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
-            expert_out = expert(tokens_for_this_expert)
-            outputs.append(expert_out)
-            start_idx = end_idx
-
-        outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
-        if self.ep_size > 1:
-            new_x = torch.empty_like(outs)
-            new_x[gatherd_idxs] = outs
-            gathered_tokens = new_x.new_empty(*sorted_tokens_shape)
-            dist.all_to_all(
-                list(gathered_tokens.split(input_split_sizes)),
-                list(new_x.split(output_splits)),
-            )
-            outs = gathered_tokens
-
-        new_x = torch.empty_like(outs)
-        new_x[idxs] = outs
-        final_out = (
-            new_x.view(*topk_ids.shape, -1)
-            .type(topk_weight.dtype)
-            .mul_(topk_weight.unsqueeze(dim=-1))
-            .sum(dim=1)
-            .type(new_x.dtype)
-        )
-        return final_out
-
-
-# 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.llama.modeling_llama.LlamaAttention with Llama->DeepseekV2
-class DeepseekV2Attention(nn.Module):
-    """Multi-headed attention from 'Attention Is All You Need' paper"""
-
-    def __init__(self, config: DeepseekV2Config, 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.attention_dropout = config.attention_dropout
-        self.hidden_size = config.hidden_size
-        self.num_heads = config.num_attention_heads
-
-        self.max_position_embeddings = config.max_position_embeddings
-        self.rope_theta = config.rope_theta
-        self.q_lora_rank = config.q_lora_rank
-        self.qk_rope_head_dim = config.qk_rope_head_dim
-        self.kv_lora_rank = config.kv_lora_rank
-        self.v_head_dim = config.v_head_dim
-        self.qk_nope_head_dim = config.qk_nope_head_dim
-        self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
-
-        self.is_causal = True
-
-        if self.q_lora_rank is None:
-            self.q_proj = nn.Linear(
-                self.hidden_size, self.num_heads * self.q_head_dim, bias=False
-            )
-        else:
-            self.q_a_proj = nn.Linear(
-                self.hidden_size, config.q_lora_rank, bias=config.attention_bias
-            )
-            self.q_a_layernorm = DeepseekV2RMSNorm(config.q_lora_rank)
-            self.q_b_proj = nn.Linear(
-                config.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
-            )
-
-        self.kv_a_proj_with_mqa = nn.Linear(
-            self.hidden_size,
-            config.kv_lora_rank + config.qk_rope_head_dim,
-            bias=config.attention_bias,
-        )
-        self.kv_a_layernorm = DeepseekV2RMSNorm(config.kv_lora_rank)
-        self.kv_b_proj = nn.Linear(
-            config.kv_lora_rank,
-            self.num_heads
-            * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
-            bias=False,
-        )
-
-        self.o_proj = nn.Linear(
-            self.num_heads * self.v_head_dim,
-            self.hidden_size,
-            bias=config.attention_bias,
-        )
-        self._init_rope()
-
-        self.softmax_scale = self.q_head_dim ** (-0.5)
-        if self.config.rope_scaling is not None:
-            mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
-            scaling_factor = self.config.rope_scaling["factor"]
-            if mscale_all_dim:
-                mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
-                self.softmax_scale = self.softmax_scale * mscale * mscale
-
-    def _init_rope(self):
-        if self.config.rope_scaling is None:
-            self.rotary_emb = DeepseekV2RotaryEmbedding(
-                self.qk_rope_head_dim,
-                max_position_embeddings=self.max_position_embeddings,
-                base=self.rope_theta,
-            )
-        else:
-            scaling_type = self.config.rope_scaling["type"]
-            scaling_factor = self.config.rope_scaling["factor"]
-            if scaling_type == "linear":
-                self.rotary_emb = DeepseekV2LinearScalingRotaryEmbedding(
-                    self.qk_rope_head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            elif scaling_type == "dynamic":
-                self.rotary_emb = DeepseekV2DynamicNTKScalingRotaryEmbedding(
-                    self.qk_rope_head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                )
-            elif scaling_type == "yarn":
-                kwargs = {
-                    key: self.config.rope_scaling[key]
-                    for key in [
-                        "original_max_position_embeddings",
-                        "beta_fast",
-                        "beta_slow",
-                        "mscale",
-                        "mscale_all_dim",
-                    ]
-                    if key in self.config.rope_scaling
-                }
-                self.rotary_emb = DeepseekV2YarnRotaryEmbedding(
-                    self.qk_rope_head_dim,
-                    max_position_embeddings=self.max_position_embeddings,
-                    scaling_factor=scaling_factor,
-                    base=self.rope_theta,
-                    **kwargs,
-                )
-            else:
-                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
-
-    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
-        return (
-            tensor.view(bsz, seq_len, self.num_heads, self.v_head_dim)
-            .transpose(1, 2)
-            .contiguous()
-        )
-
-    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,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-        bsz, q_len, _ = hidden_states.size()
-
-        if self.q_lora_rank is None:
-            q = self.q_proj(hidden_states)
-        else:
-            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
-        q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
-        q_nope, q_pe = torch.split(
-            q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
-        )
-
-        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
-        compressed_kv, k_pe = torch.split(
-            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
-        )
-        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
-        kv = (
-            self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
-            .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
-            .transpose(1, 2)
-        )
-
-        k_nope, value_states = torch.split(
-            kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
-        )
-        kv_seq_len = value_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)
-
-        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
-
-        query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
-        query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
-        query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
-
-        key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
-        key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
-        key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(
-                key_states, value_states, self.layer_idx, cache_kwargs
-            )
-
-        attn_weights = (
-            torch.matmul(query_states, key_states.transpose(2, 3)) * self.softmax_scale
-        )
-
-        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()}"
-            )
-        assert attention_mask is not None
-        if attention_mask is not None:
-            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
-                raise ValueError(
-                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
-                )
-            attn_weights = attn_weights + attention_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.v_head_dim):
-            raise ValueError(
-                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.v_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.num_heads * self.v_head_dim)
-
-        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.llama.modeling_llama.LlamaFlashAttention2 with Llama->DeepseekV2
-class DeepseekV2FlashAttention2(DeepseekV2Attention):
-    """
-    DeepseekV2 flash attention module. This module inherits from `DeepseekV2Attention` 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.
-    """
-
-    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.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Cache] = None,
-        output_attentions: bool = False,
-        use_cache: bool = False,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-        # DeepseekV2FlashAttention2 attention does not support output_attentions
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-
-            # overwrite attention_mask with padding_mask
-            attention_mask = kwargs.pop("padding_mask")
-
-        output_attentions = False
-
-        bsz, q_len, _ = hidden_states.size()
-
-        if self.q_lora_rank is None:
-            q = self.q_proj(hidden_states)
-        else:
-            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
-        q = q.view(bsz, q_len, self.num_heads, self.q_head_dim).transpose(1, 2)
-        q_nope, q_pe = torch.split(
-            q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1
-        )
-
-        # Flash attention requires the input to have the shape
-        # batch_size x seq_length x head_dim x hidden_dim
-        # therefore we just need to keep the original shape
-        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
-        compressed_kv, k_pe = torch.split(
-            compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1
-        )
-        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)
-        kv = (
-            self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
-            .view(bsz, q_len, self.num_heads, self.qk_nope_head_dim + self.v_head_dim)
-            .transpose(1, 2)
-        )
-
-        k_nope, value_states = torch.split(
-            kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1
-        )
-        kv_seq_len = value_states.shape[-2]
-
-        kv_seq_len = value_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)
-        q_pe, k_pe = apply_rotary_pos_emb(q_pe, k_pe, cos, sin, position_ids)
-
-        query_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
-        query_states[:, :, :, : self.qk_nope_head_dim] = q_nope
-        query_states[:, :, :, self.qk_nope_head_dim :] = q_pe
-
-        key_states = k_pe.new_empty(bsz, self.num_heads, q_len, self.q_head_dim)
-        key_states[:, :, :, : self.qk_nope_head_dim] = k_nope
-        key_states[:, :, :, self.qk_nope_head_dim :] = k_pe
-
-        if self.q_head_dim != self.v_head_dim:
-            value_states = F.pad(value_states, [0, self.q_head_dim - self.v_head_dim])
-
-        if past_key_value is not None:
-            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
-            key_states, value_states = past_key_value.update(
-                key_states, value_states, self.layer_idx, cache_kwargs
-            )
-
-        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
-        # to be able to avoid many of these transpose/reshape/view.
-        query_states = query_states.transpose(1, 2)
-        key_states = key_states.transpose(1, 2)
-        value_states = value_states.transpose(1, 2)
-
-        dropout_rate = self.attention_dropout if self.training else 0.0
-
-        # 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 the correct dtype just to be sure everything works as expected.
-        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
-        # in fp32. (DeepseekV2RMSNorm handles it correctly)
-
-        input_dtype = query_states.dtype
-        if input_dtype == torch.float32:
-            # Handle the case where the model is quantized
-            if hasattr(self.config, "_pre_quantization_dtype"):
-                target_dtype = self.config._pre_quantization_dtype
-            elif torch.is_autocast_enabled():
-                target_dtype = torch.get_autocast_gpu_dtype()
-            else:
-                target_dtype = self.q_proj.weight.dtype if self.q_lora_rank is None else self.q_a_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)
-
-        attn_output = self._flash_attention_forward(
-            query_states,
-            key_states,
-            value_states,
-            attention_mask,
-            q_len,
-            dropout=dropout_rate,
-            softmax_scale=self.softmax_scale,
-        )
-        if self.q_head_dim != self.v_head_dim:
-            attn_output = attn_output[:, :, :, : self.v_head_dim]
-
-        attn_output = attn_output.reshape(
-            bsz, q_len, self.num_heads * self.v_head_dim
-        ).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,
-    ):
-        """
-        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 (`int`, *optional*):
-                Attention dropout
-            softmax_scale (`float`, *optional*):
-                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
-        """
-        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 DeepseekV2FlashAttention2 __init__.
-            causal = self.is_causal and query_length != 1
-
-        # 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
-
-            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,
-            )
-
-            attn_output = pad_input(
-                attn_output_unpad, indices_q, batch_size, query_length
-            )
-        else:
-            attn_output = flash_attn_func(
-                query_states,
-                key_states,
-                value_states,
-                dropout,
-                softmax_scale=softmax_scale,
-                causal=causal,
-            )
-
-        return attn_output
-
-    def _upad_input(
-        self, query_layer, key_layer, value_layer, attention_mask, query_length
-    ):
-        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
-        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
-
-        key_layer = index_first_axis(
-            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
-            indices_k,
-        )
-        value_layer = index_first_axis(
-            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim),
-            indices_k,
-        )
-        if query_length == kv_seq_len:
-            query_layer = index_first_axis(
-                query_layer.reshape(batch_size * kv_seq_len, self.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),
-        )
-
-
-ATTENTION_CLASSES = {
-    "eager": DeepseekV2Attention,
-    "flash_attention_2": DeepseekV2FlashAttention2,
-}
-
-
-class DeepseekV2DecoderLayer(nn.Module):
-    def __init__(self, config: DeepseekV2Config, layer_idx: int):
-        super().__init__()
-        self.hidden_size = config.hidden_size
-
-        self.self_attn = ATTENTION_CLASSES[config._attn_implementation](
-            config=config, layer_idx=layer_idx
-        )
-
-        self.mlp = (
-            DeepseekV2MoE(config)
-            if (
-                config.n_routed_experts is not None
-                and layer_idx >= config.first_k_dense_replace
-                and layer_idx % config.moe_layer_freq == 0
-            )
-            else DeepseekV2MLP(config)
-        )
-        self.input_layernorm = DeepseekV2RMSNorm(
-            config.hidden_size, eps=config.rms_norm_eps
-        )
-        self.post_attention_layernorm = DeepseekV2RMSNorm(
-            config.hidden_size, eps=config.rms_norm_eps
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        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,
-        use_cache: Optional[bool] = False,
-        **kwargs,
-    ) -> 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_size, sequence_length)` if flash attention is used or `(batch_size, 1,
-                query_sequence_length, key_sequence_length)` if default attention is used.
-            output_attentions (`bool`, *optional*):
-                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
-                returned tensors for more detail.
-            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
-        """
-        if "padding_mask" in kwargs:
-            warnings.warn(
-                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
-            )
-        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,
-            **kwargs,
-        )
-        hidden_states = residual + hidden_states
-
-        # Fully Connected
-        residual = hidden_states
-        hidden_states = self.post_attention_layernorm(hidden_states)
-        hidden_states = self.mlp(hidden_states)
-        hidden_states = residual + hidden_states
-
-        outputs = (hidden_states,)
-
-        if output_attentions:
-            outputs += (self_attn_weights,)
-
-        if use_cache:
-            outputs += (present_key_value,)
-
-        return outputs
-
-
-DeepseekV2_START_DOCSTRING = r"""
-    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
-    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
-    etc.)
-
-    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
-    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
-    and behavior.
-
-    Parameters:
-        config ([`DeepseekV2Config`]):
-            Model configuration class with all the parameters of the model. Initializing with a config file does not
-            load the weights associated with the model, only the configuration. Check out the
-            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
-"""
-
-
-@add_start_docstrings(
-    "The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
-    DeepseekV2_START_DOCSTRING,
-)
-class DeepseekV2PreTrainedModel(PreTrainedModel):
-    config_class = DeepseekV2Config
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["DeepseekV2DecoderLayer"]
-    _skip_keys_device_placement = "past_key_values"
-    _supports_flash_attn_2 = 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_()
-
-
-DeepseekV2_INPUTS_DOCSTRING = r"""
-    Args:
-        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
-            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
-            it.
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            [What are input IDs?](../glossary#input-ids)
-        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
-
-            - 1 for tokens that are **not masked**,
-            - 0 for tokens that are **masked**.
-
-            [What are attention masks?](../glossary#attention-mask)
-
-            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
-            [`PreTrainedTokenizer.__call__`] for details.
-
-            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
-            `past_key_values`).
-
-            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
-            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
-            information on the default strategy.
-
-            - 1 indicates the head is **not masked**,
-            - 0 indicates the head is **masked**.
-        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
-            config.n_positions - 1]`.
-
-            [What are position IDs?](../glossary#position-ids)
-        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
-            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
-            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
-            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
-
-            Two formats are allowed:
-            - a [`~cache_utils.Cache`] instance;
-            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
-            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
-            cache format.
-
-            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
-            legacy cache format will be returned.
-
-            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
-            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
-            of shape `(batch_size, sequence_length)`.
-        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
-            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
-            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
-            model's internal embedding lookup matrix.
-        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`).
-        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_hidden_states (`bool`, *optional*):
-            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
-            more detail.
-        return_dict (`bool`, *optional*):
-            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
-"""
-
-
-@add_start_docstrings(
-    "The bare DeepseekV2 Model outputting raw hidden-states without any specific head on top.",
-    DeepseekV2_START_DOCSTRING,
-)
-class DeepseekV2Model(DeepseekV2PreTrainedModel):
-    """
-    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DeepseekV2DecoderLayer`]
-
-    Args:
-        config: DeepseekV2Config
-    """
-
-    def __init__(self, config: DeepseekV2Config):
-        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(
-            [
-                DeepseekV2DecoderLayer(config, layer_idx)
-                for layer_idx in range(config.num_hidden_layers)
-            ]
-        )
-        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
-        self.norm = DeepseekV2RMSNorm(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
-
-    @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: 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,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, BaseModelOutputWithPast]:
-        output_attentions = (
-            output_attentions
-            if output_attentions is not None
-            else self.config.output_attentions
-        )
-        output_hidden_states = (
-            output_hidden_states
-            if output_hidden_states is not None
-            else self.config.output_hidden_states
-        )
-        use_cache = use_cache if use_cache is not None else self.config.use_cache
-
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        # retrieve input_ids and inputs_embeds
-        if input_ids is not None and inputs_embeds is not None:
-            raise ValueError(
-                "You cannot specify both input_ids and inputs_embeds at the same time"
-            )
-        elif input_ids is not None:
-            batch_size, seq_length = input_ids.shape[:2]
-        elif inputs_embeds is not None:
-            batch_size, seq_length = inputs_embeds.shape[:2]
-        else:
-            raise ValueError("You have to specify either 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`transformers."
-                )
-                use_cache = False
-
-        past_key_values_length = 0
-        if use_cache:
-            use_legacy_cache = not isinstance(past_key_values, Cache)
-            if use_legacy_cache:
-                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
-            past_key_values_length = past_key_values.get_usable_length(seq_length)
-
-        if position_ids is None:
-            device = input_ids.device if input_ids is not None else inputs_embeds.device
-            position_ids = torch.arange(
-                past_key_values_length,
-                seq_length + past_key_values_length,
-                dtype=torch.long,
-                device=device,
-            )
-            position_ids = position_ids.unsqueeze(0)
-
-        if inputs_embeds is None:
-            inputs_embeds = self.embed_tokens(input_ids)
-
-        if self._use_flash_attention_2:
-            # 2d mask is passed through the layers
-            attention_mask = (
-                attention_mask
-                if (attention_mask is not None and 0 in attention_mask)
-                else None
-            )
-        else:
-            # 4d mask is passed through the layers
-            attention_mask = _prepare_4d_causal_attention_mask(
-                attention_mask,
-                (batch_size, seq_length),
-                inputs_embeds,
-                past_key_values_length,
-            )
-
-        # embed positions
-        hidden_states = inputs_embeds
-
-        # decoder layers
-        all_hidden_states = () if output_hidden_states else None
-        all_self_attns = () if output_attentions 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,
-                    attention_mask,
-                    position_ids,
-                    past_key_values,
-                    output_attentions,
-                    use_cache,
-                )
-            else:
-                layer_outputs = decoder_layer(
-                    hidden_states,
-                    attention_mask=attention_mask,
-                    position_ids=position_ids,
-                    past_key_value=past_key_values,
-                    output_attentions=output_attentions,
-                    use_cache=use_cache,
-                )
-
-            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],)
-
-        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]
-                if v is not None
-            )
-        return BaseModelOutputWithPast(
-            last_hidden_state=hidden_states,
-            past_key_values=next_cache,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attns,
-        )
-
-
-class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel):
-    _tied_weights_keys = ["lm_head.weight"]
-
-    def __init__(self, config):
-        super().__init__(config)
-        self.model = DeepseekV2Model(config)
-        self.vocab_size = config.vocab_size
-        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    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
-
-    @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
-    @replace_return_docstrings(
-        output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC
-    )
-    def forward(
-        self,
-        input_ids: 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,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, CausalLMOutputWithPast]:
-        r"""
-        Args:
-            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
-                Labels for computing the masked language modeling loss. Indices should either be in `[0, transformers.,
-                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
-                (masked), the loss is only computed for the tokens with labels in `[0, transformers., config.vocab_size]`.
-
-        Returns:
-
-        Example:
-
-        ```python
-        >>> from transformers import AutoTokenizer, DeepseekV2ForCausalLM
-
-        >>> model = DeepseekV2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
-        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
-
-        >>> prompt = "Hey, are you conscious? Can you talk to me?"
-        >>> inputs = tokenizer(prompt, return_tensors="pt")
-
-        >>> # Generate
-        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
-        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
-        ```"""
-        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
-        )
-        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,
-            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,
-            return_dict=return_dict,
-        )
-
-        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)
-
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return (loss,) + output if loss is not None else output
-
-        return CausalLMOutputWithPast(
-            loss=loss,
-            logits=logits,
-            past_key_values=outputs.past_key_values,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )
-
-    def prepare_inputs_for_generation(
-        self,
-        input_ids,
-        past_key_values=None,
-        attention_mask=None,
-        inputs_embeds=None,
-        **kwargs,
-    ):
-        if past_key_values is not None:
-            if isinstance(past_key_values, Cache):
-                cache_length = past_key_values.get_seq_length()
-                past_length = past_key_values.seen_tokens
-                max_cache_length = past_key_values.get_max_length()
-            else:
-                cache_length = past_length = past_key_values[0][0].shape[2]
-                max_cache_length = None
-
-            # 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 exclusivelly 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_key_values is None:
-            model_inputs = {"inputs_embeds": inputs_embeds}
-        else:
-            model_inputs = {"input_ids": input_ids}
-
-        model_inputs.update(
-            {
-                "position_ids": position_ids,
-                "past_key_values": past_key_values,
-                "use_cache": kwargs.get("use_cache"),
-                "attention_mask": attention_mask,
-            }
-        )
-        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
-
-
-@add_start_docstrings(
-    """
-    The DeepseekV2 Model transformer with a sequence classification head on top (linear layer).
-
-    [`DeepseekV2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
-    (e.g. GPT-2) do.
-
-    Since it does classification on the last token, it requires to know the position of the last token. If a
-    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
-    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
-    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
-    each row of the batch).
-    """,
-    DeepseekV2_START_DOCSTRING,
-)
-class DeepseekV2ForSequenceClassification(DeepseekV2PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = DeepseekV2Model(config)
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
-
-        # 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
-
-    @add_start_docstrings_to_model_forward(DeepseekV2_INPUTS_DOCSTRING)
-    def forward(
-        self,
-        input_ids: 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,
-        return_dict: Optional[bool] = None,
-    ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, transformers.,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-        return_dict = (
-            return_dict if return_dict is not None else self.config.use_return_dict
-        )
-
-        transformer_outputs = self.model(
-            input_ids,
-            attention_mask=attention_mask,
-            position_ids=position_ids,
-            past_key_values=past_key_values,
-            inputs_embeds=inputs_embeds,
-            use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-        hidden_states = transformer_outputs[0]
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.pad_token_id is None and batch_size != 1:
-            raise ValueError(
-                "Cannot handle batch sizes > 1 if no padding token is defined."
-            )
-        if self.config.pad_token_id is None:
-            sequence_lengths = -1
-        else:
-            if input_ids is not None:
-                sequence_lengths = (
-                    torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
-                ).to(logits.device)
-            else:
-                sequence_lengths = -1
-
-        pooled_logits = logits[
-            torch.arange(batch_size, device=logits.device), sequence_lengths
-        ]
-
-        loss = None
-        if labels is not None:
-            labels = labels.to(logits.device)
-            if self.config.problem_type is None:
-                if self.num_labels == 1:
-                    self.config.problem_type = "regression"
-                elif self.num_labels > 1 and (
-                    labels.dtype == torch.long or labels.dtype == torch.int
-                ):
-                    self.config.problem_type = "single_label_classification"
-                else:
-                    self.config.problem_type = "multi_label_classification"
-
-            if self.config.problem_type == "regression":
-                loss_fct = MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(pooled_logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = CrossEntropyLoss()
-                loss = loss_fct(
-                    pooled_logits.view(-1, self.num_labels), labels.view(-1)
-                )
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = BCEWithLogitsLoss()
-                loss = loss_fct(pooled_logits, labels)
-        if not return_dict:
-            output = (pooled_logits,) + transformer_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )