optimum/habana/transformers/models/deepseek_v2/modeling_deepseek_v2.py

# 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 DeepSeekV2 model. Adapted from https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite/resolve/main/modeling_deepseek.py"""

import math
import os
import warnings
from typing import List, Optional, Tuple, Union

import habana_frameworks.torch.core as htcore
import torch
import torch.distributed as dist

# 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.
import torch.fx
import torch.nn.functional as F
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers import PretrainedConfig
from transformers.activations import ACT2FN
from transformers.cache_utils import Cache, DynamicCache, StaticCache
from transformers.generation import GenerationMixin
from transformers.integrations.deepspeed import is_deepspeed_available
from transformers.modeling_attn_mask_utils import (
    _prepare_4d_causal_attention_mask,
)
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    MoeCausalLMOutputWithPast,
    MoeModelOutputWithPast,
    SequenceClassifierOutputWithPast,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.pytorch_utils import (
    ALL_LAYERNORM_LAYERS,
)
from transformers.utils import (
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
)

from ....distributed.tensorparallel import _all_reduce
from ...modeling_attn_mask_utils import _gaudi_prepare_4d_causal_attention_mask
from .configuration_deepseek_v2 import DeepseekV2Config


_prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)

logger = logging.get_logger(__name__)

_CONFIG_FOR_DOC = "DeepseekV2Config"

# default expert number per slice for dynamic MoE
SLICE_MAX_EXPERT = 80

try:
    from habana_frameworks.torch.hpex.kernels import RotaryPosEmbeddingHelperV2 as FusedRoPE

    print("Using HPU fused kernel for apply_rotary_pos_emb")
except ImportError:
    print("Not using HPU fused kernel for apply_rotary_pos_emb")
    FusedRoPE = None

try:
    from habana_frameworks.torch.hpex.normalization import FusedRMSNorm

    print("Using HPU fused kernel for RMSNorm")
except ImportError:
    print("Not using HPU fused kernel for RMSNorm")
    FusedRMSNorm = None

try:
    from habana_frameworks.torch.hpex.kernels import FusedSDPA
except ImportError:
    print("Not using HPU fused scaled dot-product attention kernel.")
    FusedSDPA = None

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,
    )


# Copied from transformers.models.mixtral.modeling_mixtral.load_balancing_loss_func
def load_balancing_loss_func(
    gate_logits: Union[torch.Tensor, Tuple[torch.Tensor], None],
    num_experts: Optional[int] = None,
    top_k=2,
    attention_mask: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, int]:
    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:
            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
            shape [batch_size X sequence_length, num_experts].
        num_experts:
            Number of experts
        top_k:
            The number of experts to route per-token, can be also interpreted as the `top-k` routing
            parameter.
        attention_mask (`torch.Tensor`, *optional*):
            The attention_mask used in forward function
            shape [batch_size X sequence_length] if not None.

    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


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):
        if hidden_states.device.type == "hpu" and FusedRMSNorm:
            # mixed dtypes are not good for FusedRMSNorm, both inputs need to have same dtype
            if hidden_states.dtype != self.weight.dtype:
                orig_dtype = hidden_states.dtype
                hidden_states = FusedRMSNorm.apply(
                    hidden_states.to(self.weight.dtype), self.weight, self.variance_epsilon
                )
                return hidden_states.to(orig_dtype)
            else:
                hidden_states = FusedRMSNorm.apply(hidden_states, self.weight, self.variance_epsilon)
                return hidden_states
        else:
            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.max_seq_len_cached = max_position_embeddings
        self._set_cos_sin_cache(
            seq_len=self.max_seq_len_cached,
            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=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 seq_len is not None and 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)
        emb_cos = (emb.cos() * _mscale).to(dtype)
        emb_sin = (emb.sin() * _mscale).to(dtype)
        self.register_buffer("cos_cached", emb_cos, persistent=False)
        self.register_buffer("sin_cached", emb_sin, persistent=False)


def apply_customized_rope(q, k, cos, sin, position_ids):
    if q.device.type == "hpu" and FusedRoPE:
        return FusedRoPE.apply(
            q, cos.unsqueeze(0).unsqueeze(0), sin.unsqueeze(0).unsqueeze(0), position_ids
        ), FusedRoPE.apply(k, cos.unsqueeze(0).unsqueeze(0), sin.unsqueeze(0).unsqueeze(0), position_ids)
    else:
        return apply_rotary_pos_emb(q, k, cos, sin, position_ids)


# 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: torch.Tensor, cos, sin, position_ids, unsqueeze_dim=1):
    """Applies Rotary Position Embedding to the query and key tensors.
    Args:
        q (`torch.Tensor`): The query 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.
    """

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

    if q.device.type == "hpu" and FusedRoPE:
        return FusedRoPE.apply(
            q, cos.unsqueeze(0).unsqueeze(0).clone(), sin.unsqueeze(0).unsqueeze(0).clone(), position_ids
        )
    else:
        cos = cos[position_ids].unsqueeze(unsqueeze_dim)
        sin = sin[position_ids].unsqueeze(unsqueeze_dim)
        q_embed = (q * cos) + (rotate_half(q) * sin)
        return q_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.bfloat16), self.weight.type(torch.bfloat16), None).to(
            dtype=torch.float32
        )
        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=True)
        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=True)[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=True)

        ### 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
        self.experts_per_rank = config.n_routed_experts
        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)

        self.expert_slice = math.ceil(self.experts_per_rank / SLICE_MAX_EXPERT)
        self.expert_chunk = math.ceil(self.experts_per_rank / self.expert_slice)

    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])
        # we cast back to the input dtype
        topk_weight = topk_weight.to(hidden_states.dtype)
        batch = orig_shape[0]
        sequence_length = orig_shape[1]
        hidden_dim = orig_shape[2]
        if self.training:
            padded_weights = torch.zeros(
                (batch * sequence_length, self.config.n_routed_experts),
                dtype=topk_weight.dtype,
                device=topk_weight.device,
            )
            padded_weights.scatter_(-1, topk_idx, topk_weight)
            padded_weights = padded_weights.reshape(-1, sequence_length, self.config.n_routed_experts)
            padded_weights = padded_weights.permute(2, 0, 1).unsqueeze(-1)

            final_hidden_states = torch.zeros(
                (batch, sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
            )
            for i, expert in enumerate(self.experts):
                current_hidden_state = expert(hidden_states)
                current_padded_weight = padded_weights[i]
                final_hidden_states = (
                    final_hidden_states
                    + current_hidden_state.reshape(-1, sequence_length, hidden_dim) * current_padded_weight
                )
            final_hidden_states = final_hidden_states.type(hidden_states.dtype)
            final_hidden_states = final_hidden_states.view(*orig_shape)
            final_hidden_states = AddAuxiliaryLoss.apply(final_hidden_states, aux_loss)
        else:
            final_hidden_states = torch.zeros(
                (batch * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
            )
            for idx in range(self.expert_slice):
                experts_min = (self.ep_rank * self.experts_per_rank) + (self.expert_chunk * idx)
                experts_max = min((experts_min + self.expert_chunk), (self.ep_rank + 1) * self.experts_per_rank)
                experts_range = range(experts_min, experts_max)
                gate_proj_list = [self.experts[i].gate_proj.weight.squeeze() for i in experts_range]
                down_proj_list = [self.experts[i].down_proj.weight.squeeze() for i in experts_range]
                up_proj_list = [self.experts[i].up_proj.weight.squeeze() for i in experts_range]
                hidden_states_slice = torch.ops.hpu.mixture_of_experts(
                    hidden_states=hidden_states,
                    expert_routing_table=topk_idx,
                    router_weights=topk_weight,
                    w1=gate_proj_list,
                    w2=up_proj_list,
                    w3=down_proj_list,
                    permuted_weights=True,
                    activation="silu",
                    experts_min=experts_min,
                    experts_max=experts_max - 1,
                )
                final_hidden_states = final_hidden_states + hidden_states_slice
                htcore.mark_step()

            if self.ep_size > 1:
                final_hidden_states = _all_reduce(final_hidden_states)
            elif is_deepspeed_available():
                from deepspeed import comm as dist

                if dist.is_initialized():
                    dist.all_reduce(final_hidden_states, op=dist.ReduceOp.SUM)

            final_hidden_states = final_hidden_states.type(hidden_states.dtype)
            final_hidden_states = final_hidden_states.reshape(-1, sequence_length, hidden_dim)

        if self.config.n_shared_experts is not None:
            final_hidden_states = final_hidden_states + self.shared_experts(identity)

        return final_hidden_states


class Matmul(torch.nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x, y):
        return torch.matmul(x, y)


def gaudi_deepseekv2_repeat_kv(
    query_states: torch.Tensor,
    key_states: torch.Tensor,
    value_states: torch.Tensor,
    attention_mask: torch.Tensor,
    n_rep: int,
):
    """
    Copied from repeat_kv: https://github.com/huggingface/transformers/blob/v4.37.0/src/transformers/models/mixtral/modeling_mixtral.py
    The only differences are:
    - Append num_key_value_heads == 1 check as kv states can be broadcasted during matmuls so need to expand and reshape them.
    - Add new args query_states, key_states, value_states and attention_mask and update the logic for expansion.
    The query states go from (batch, num_heads, seqlen, head_dim) to (batch, num_key_value_heads, n_rep, seqlen, head_dim)
    The key/value states go from (batch, num_key_value_heads, seqlen, head_dim) to (batch, num_key_value_heads, 1, seqlen, head_dim)
    """
    batch, num_key_value_heads, kv_len, head_dim = key_states.shape
    if n_rep == 1 or num_key_value_heads == 1:
        return query_states, key_states, value_states, attention_mask

    new_kv_shape = (batch, num_key_value_heads, 1, kv_len, head_dim)
    key_states = key_states.reshape(new_kv_shape)
    value_states = value_states.reshape(new_kv_shape)

    batch, q_heads, q_len, head_dim = query_states.shape
    new_q_shape = (batch, num_key_value_heads, n_rep, q_len, head_dim)
    query_states = query_states.reshape(new_q_shape)

    if attention_mask is not None:
        # Add groups dim and set to 1
        attention_mask = attention_mask.unsqueeze(1)

    return query_states, key_states, value_states, attention_mask


class KVCache(torch.nn.Module):
    def __init__(self):
        super(KVCache, self).__init__()
        self.cache = None
        self.inp_seq_len = -1

    def allocate(self, inp_seq_len, dtype, device, shape):
        if self.cache is None or self.cache.shape != shape:
            self.inp_seq_len = inp_seq_len
            self.cache = torch.zeros(shape, dtype=dtype, device=device)
        else:
            assert self.inp_seq_len == inp_seq_len, (
                f"inp_seq_len must be the same. self.inp_seq_len:{self.inp_seq_len} inp_seq_len:{inp_seq_len}"
            )
            self.cache.fill_(0)

    def update(self, prev, cur, dim, idx, inp_seq_len):
        orig_cur = cur
        if prev.shape == cur.shape:
            prev.copy_(cur)
            return orig_cur
        if cur.shape[1] > 1 and cur.shape[1] <= prev.shape[1]:
            # Initialize
            prev[:, :inp_seq_len, :].copy_(cur)
            return orig_cur
        assert cur.shape[1] == 1, f"Cannot update kv-cache. Unsupported shapes. prev:{prev.shape} cur:{cur.shape}"

        if idx is not None:
            prev.index_copy_(dim, idx - 1, cur)
            return prev
        else:
            return torch.cat((prev, cur), dim=dim)

    def get_shape(self):
        if self.cache is None:
            return None
        return self.cache.shape

    def forward(self, cur, dim, idx):
        return self.update(self.cache, cur, dim, idx, self.inp_seq_len)


class ModuleFusedSDPA(torch.nn.Module):
    def __init__(self, fusedSDPA, scale, attention_dropout, enable_recompute, flash_attention_fp8):
        super().__init__()
        self._hpu_kernel_fsdpa = fusedSDPA
        self.scale = scale
        self.attention_dropout = attention_dropout
        self.enable_recompute = enable_recompute
        self.flash_attention_fp8 = flash_attention_fp8

    def forward(
        self,
        query,
        key,
        value,
        attn_mask,
        dropout_p,
        is_casual,
        scale,
        softmax_mode,
        recompute_mode,
        valid_sequence_lengths,
        padding_side="left",
    ):
        return self._hpu_kernel_fsdpa.apply(
            query,
            key,
            value,
            attn_mask,
            dropout_p,
            is_casual,
            scale,
            softmax_mode,
            recompute_mode,
            valid_sequence_lengths,
            padding_side,
        )


# 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.num_key_value_groups = self.num_heads // config.num_key_value_heads
        self.matmul_qk = Matmul()
        self.matmul_av = Matmul()
        self.k_cache = KVCache()
        self.v_cache = KVCache()
        self.inp_seq_len = -1

        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

        self.norm_factor = self.softmax_scale
        self.fused_scaled_dot_product_attention = (
            ModuleFusedSDPA(
                FusedSDPA,
                scale=self.norm_factor,
                attention_dropout=self.attention_dropout,
                enable_recompute=False,
                flash_attention_fp8=getattr(config, "flash_attention_fp8", False),
            )
            if FusedSDPA
            else None
        )

    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 allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
        compressed_kv_cache_shape = (batch_size, max_seq_len, self.kv_lora_rank)
        k_pe_cache_shape = (batch_size, max_seq_len, self.qk_rope_head_dim)
        device = self.kv_a_proj_with_mqa.weight.device
        dtype = self.config.torch_dtype

        self.k_cache.allocate(inp_seq_len, dtype, device, compressed_kv_cache_shape)
        self.v_cache.allocate(inp_seq_len, dtype, device, k_pe_cache_shape)

    def update_sincos_cache(self, seq_len):
        # Call rotary emb forward() to update cos/sin cache when infering more than self.max_position_embeddings
        # This helps in avoiding creation of these caches during actual model forward pass and
        # reduce memory consumption and improve performance.
        if seq_len > self.max_position_embeddings:
            self.max_position_embeddings = seq_len
            _, _ = self.rotary_emb(self.k_b_proj.weight, seq_len=seq_len)

    def reorder(self, tensor, beam_idx, dim_a, dim_b):
        updated = tensor.index_select(0, beam_idx)
        tensor.copy_(updated)

    def reorder_kv_cache(self, beam_idx: torch.LongTensor):
        if self.k_cache.cache is None:
            return (None, None)

        head_dim = self.k_cache.cache.size(-1)
        seq_length = self.k_cache.cache.size(-2)
        self.reorder(self.k_cache.cache, beam_idx, seq_length, head_dim)
        self.reorder(self.v_cache.cache, beam_idx, seq_length, head_dim)
        return (self.k_cache.cache.shape, self.v_cache.cache.shape)

    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 split_kv_b_proj(self):
        kv_b_proj_weight = self.kv_b_proj.weight.view(self.num_heads, -1, self.kv_lora_rank)
        self.q_absorb = kv_b_proj_weight[:, : self.qk_nope_head_dim, :].unsqueeze(0).transpose(0, 1)
        self.out_absorb = kv_b_proj_weight[:, self.qk_nope_head_dim :, :].unsqueeze(0)

    def compress_kv(
        self,
        hidden_states_kv: torch.Tensor,
        kv_position_ids: torch.LongTensor,
    ) -> torch.Tensor:
        # return the RoPE'ed & compressed kv
        bsz, kv_seq_len, _ = hidden_states_kv.size()
        compressed_kv = self.kv_a_proj_with_mqa(hidden_states_kv)
        compressed_kv, k_pe = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
        compressed_kv = self.kv_a_layernorm(compressed_kv)
        k_pe = k_pe.view(bsz, kv_seq_len, 1, self.qk_rope_head_dim).transpose(1, 2)
        cos, sin = self.rotary_emb.cos_cached, self.rotary_emb.sin_cached
        k_pe = apply_rotary_pos_emb(k_pe, cos, sin, kv_position_ids).view(bsz, kv_seq_len, self.qk_rope_head_dim)
        return compressed_kv, k_pe

    def train_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,
        token_idx: Optional[torch.Tensor] = None,
        cache_position: Optional[torch.LongTensor] = None,
        attn_softmax_bf16: Optional[bool] = False,
        use_flash_attention: Optional[bool] = False,
        flash_attention_recompute: Optional[bool] = False,
        flash_attention_causal_mask: Optional[bool] = False,
        flash_attention_fast_softmax: Optional[bool] = False,
        valid_sequence_lengths: Optional[torch.Tensor] = None,
        num_virtual_tokens: int = None,
        **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."
                )

            if token_idx is None:
                if hasattr(past_key_value, "get_usable_length"):
                    kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
                else:
                    kv_seq_len += past_key_value[0].shape[-2]
            else:
                if num_virtual_tokens is not None and num_virtual_tokens == past_key_value[0].shape[-2]:
                    kv_seq_len = past_key_value[0].shape[-2] + kv_seq_len
                else:
                    kv_seq_len = past_key_value[0].shape[-2]

        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        q_pe, k_pe = apply_customized_rope(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)
        # optimization
        if use_flash_attention and FusedSDPA is not None:
            softmax_mode = "fast" if flash_attention_fast_softmax else "None"
            if flash_attention_causal_mask:
                attn_output = self.fused_scaled_dot_product_attention(
                    query_states,
                    key_states,
                    value_states,
                    None,
                    0.0,
                    True,
                    None,
                    softmax_mode,
                    flash_attention_recompute,
                    valid_sequence_lengths,
                    "left",
                )
            else:
                attn_output = self.fused_scaled_dot_product_attention(
                    query_states,
                    key_states,
                    value_states,
                    attention_mask,
                    0.0,
                    False,
                    None,
                    softmax_mode,
                    flash_attention_recompute,
                    None,
                    "None",
                )
        else:
            query_states, key_states, value_states, attention_mask = gaudi_deepseekv2_repeat_kv(
                query_states, key_states, value_states, attention_mask, self.num_key_value_groups
            )
            attn_weights = self.matmul_qk(query_states, key_states.transpose(-2, -1)) * self.softmax_scale
            htcore.mark_step()

            if attention_mask is not None:  # no matter the length, we just slice it
                causal_mask = attention_mask
                if cache_position is not None:
                    causal_mask = attention_mask[:, :, cache_position, : key_states.shape[-2]]
                attn_weights = attn_weights + causal_mask.float()

            if attn_softmax_bf16:
                attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=query_states.dtype)
            else:
                # upcast attention to fp32
                attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(
                    query_states.dtype
                )
            attn_weights = torch.nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
            attn_output = self.matmul_av(attn_weights, value_states)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value

    def prefill_forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        use_cache: bool = False,
        token_idx: Optional[torch.Tensor] = None,
        reuse_cache: Optional[bool] = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        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)
        compressed_kv = self.kv_a_layernorm(compressed_kv)
        k_pe = k_pe.view(bsz, q_len, 1, self.qk_rope_head_dim).transpose(1, 2)

        cos, sin = self.rotary_emb.cos_cached, self.rotary_emb.sin_cached
        k_pe_cache = apply_rotary_pos_emb(k_pe, cos, sin, position_ids).view(bsz, q_len, self.qk_rope_head_dim)

        kv = (
            self.kv_b_proj(compressed_kv)  # 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]
        assert kv_seq_len == q_len
        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."
                )
            if token_idx is None:
                if hasattr(past_key_value, "get_usable_length"):
                    kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
                else:
                    kv_seq_len += past_key_value[0].shape[-2]
            else:
                if reuse_cache:
                    kv_seq_len = past_key_value[0][-2]
                else:
                    kv_seq_len = past_key_value[0].shape[-2]
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        q_pe, k_pe = apply_customized_rope(q_pe, k_pe, cos, sin, position_ids)
        # update & get all compressed_kv, k_pe
        if use_cache:
            k_pe_cache = k_pe_cache.view(bsz, q_len, self.qk_rope_head_dim)  # k_pe.squeeze(1)

            if reuse_cache:
                compressed_kv = self.k_cache(compressed_kv, 1, token_idx)

                k_pe_cache = self.v_cache(k_pe_cache, 1, token_idx)
                past_key_value = (self.k_cache.get_shape(), self.v_cache.get_shape())

            else:
                if past_key_value is None:
                    dtype_1 = hidden_states.dtype
                    device_1 = hidden_states.device
                    past_key = torch.zeros(compressed_kv.shape, dtype=dtype_1, device=device_1)
                    past_value = torch.zeros(k_pe_cache.shape, dtype=dtype_1, device=device_1)
                    past_key_value = (past_key, past_value)
                compressed_kv = self.k_cache.update(past_key_value[0], compressed_kv, 1, token_idx, self.inp_seq_len)

                k_pe_cache = self.v_cache.update(past_key_value[1], k_pe_cache, 1, token_idx, self.inp_seq_len)

                if token_idx is None:
                    past_key_value = (compressed_kv, k_pe_cache)
        else:
            past_key_value = None

        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

        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)

        return attn_output, attn_weights, past_key_value

    def decode_forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        use_cache: bool = False,
        token_idx: Optional[torch.Tensor] = None,
        reuse_cache: Optional[bool] = False,
        cache_idx: int = None,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        hidden_states_q = hidden_states
        hidden_states_kv = hidden_states
        self.split_kv_b_proj()
        q_position_ids = position_ids
        kv_position_ids = position_ids
        bsz, q_len, _ = hidden_states_q.size()

        if self.q_lora_rank is None:
            q = self.q_proj(hidden_states_q)
        else:
            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states_q)))

        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)

        kv_seq_len = q_pe.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."
                )
            if token_idx is None:
                if hasattr(past_key_value, "get_usable_length"):
                    kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
                else:
                    kv_seq_len += past_key_value[0].shape[-2]
            else:
                if reuse_cache:
                    kv_seq_len = past_key_value[0][-2]
                else:
                    kv_seq_len = past_key_value[0].shape[-2]

        cos, sin = self.rotary_emb(q_pe, seq_len=kv_seq_len)
        q_pe = apply_rotary_pos_emb(q_pe, cos, sin, q_position_ids)
        q_nope = torch.matmul(q_nope.transpose(0, 1), self.q_absorb).transpose(0, 1)
        compressed_kv, k_pe = self.compress_kv(hidden_states_kv, kv_position_ids)

        # update & get all compressed_kv, k_pe
        if use_cache:
            if reuse_cache:
                if past_key_value is not None and isinstance(past_key_value[0], torch.Tensor):
                    # prefix tuning case. attach past_key_value to generate first token.
                    compressed_kv = torch.cat((past_key_value[0], compressed_kv), -2)
                    k_pe = torch.cat((past_key_value[1], k_pe), -2)

                compressed_kv = self.k_cache(compressed_kv, 1, token_idx)

                k_pe = self.v_cache(k_pe, 1, token_idx)
                past_key_value = (self.k_cache.get_shape(), self.v_cache.get_shape())

            else:
                if past_key_value is None:
                    dtype_1 = hidden_states.dtype
                    device_1 = hidden_states.device
                    past_key = torch.zeros(compressed_kv.shape, dtype=dtype_1, device=device_1)
                    past_value = torch.zeros(k_pe.shape, dtype=dtype_1, device=device_1)
                    past_key_value = (past_key, past_value)
                compressed_kv = self.k_cache.update(past_key_value[0], compressed_kv, 1, token_idx, self.inp_seq_len)

                k_pe = self.v_cache.update(past_key_value[1], k_pe, 1, token_idx, self.inp_seq_len)

                if token_idx is None:
                    past_key_value = (compressed_kv, k_pe)

            if cache_idx is not None and q_len == 1:
                compressed_kv = compressed_kv[:, :cache_idx, :]

                k_pe = k_pe[:, :cache_idx, :]
                if attention_mask is not None:
                    attention_mask = attention_mask[:, :, :, :cache_idx]

                kv_seq_len = compressed_kv.shape[-2]
        else:
            past_key_value = None

        kv_seq_len = compressed_kv.size(1)

        k_pe = k_pe.view(bsz, 1, kv_seq_len, self.qk_rope_head_dim)

        attn_weights = (
            torch.matmul(q_pe, k_pe.mT) + torch.matmul(q_nope, compressed_kv.unsqueeze(-3).mT)
        ) * self.softmax_scale

        assert attention_mask is not None
        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()}"
            )

        attn_weights = attn_weights + attention_mask

        # upcast attention to fp32
        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(q_nope.dtype)

        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
        attn_output = torch.einsum("bhql,blc->bhqc", attn_weights, compressed_kv)

        attn_output = torch.matmul(attn_output.permute(2, 1, 0, 3), self.out_absorb.mT).permute(2, 1, 0, 3)
        return attn_output, attn_weights, past_key_value

    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,
        token_idx: Optional[torch.Tensor] = None,
        reuse_cache: Optional[bool] = False,
        cache_idx: int = None,
        cache_position: Optional[torch.LongTensor] = None,
        attn_softmax_bf16: Optional[bool] = False,
        use_flash_attention: Optional[bool] = False,
        flash_attention_recompute: Optional[bool] = False,
        flash_attention_causal_mask: Optional[bool] = False,
        flash_attention_fast_softmax: Optional[bool] = False,
        valid_sequence_lengths: Optional[torch.Tensor] = None,
        num_virtual_tokens: int = None,
        first_token: Optional[bool] = True,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        """
        Attention masks and past cache are removed.
        Input:
        - hidden_states: [bsz, q_len, hidden_size]
        - position_ids: [bsz, q_len]
        """

        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.training:
            attn_output, attn_weights, past_key_value = self.train_forward(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                token_idx=token_idx,
                cache_position=cache_position,
                attn_softmax_bf16=attn_softmax_bf16,
                use_flash_attention=use_flash_attention,
                flash_attention_recompute=flash_attention_recompute,
                flash_attention_causal_mask=flash_attention_causal_mask,
                flash_attention_fast_softmax=flash_attention_fast_softmax,
                valid_sequence_lengths=valid_sequence_lengths,
                num_virtual_tokens=num_virtual_tokens,
                **kwargs,
            )

        elif first_token:  # prefill MHA
            attn_output, attn_weights, past_key_value = self.prefill_forward(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=past_key_value,
                use_cache=use_cache,
                token_idx=token_idx,
                reuse_cache=reuse_cache,
            )

        else:  # decode MLA
            attn_output, attn_weights, past_key_value = self.decode_forward(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=past_key_value,
                use_cache=use_cache,
                token_idx=token_idx,
                reuse_cache=reuse_cache,
                cache_idx=cache_idx,
            )

        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


class DeepseekV2DecoderLayer(nn.Module):
    def __init__(self, config: DeepseekV2Config, layer_idx: int):
        super().__init__()
        self.hidden_size = config.hidden_size

        self.self_attn = DeepseekV2Attention(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 allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
        self.self_attn.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len)

    def reorder_kv_cache(self, beam_idx: torch.LongTensor):
        return self.self_attn.reorder_kv_cache(beam_idx)

    def update_sincos_cache(self, seq_len):
        self.self_attn.update_sincos_cache(seq_len)

    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,
        token_idx: Optional[torch.Tensor] = None,
        reuse_cache: Optional[bool] = False,
        cache_idx: int = None,
        cache_position: Optional[torch.LongTensor] = None,
        attn_softmax_bf16: Optional[bool] = False,
        use_flash_attention: Optional[bool] = False,
        flash_attention_recompute: Optional[bool] = False,
        flash_attention_causal_mask: Optional[bool] = False,
        flash_attention_fast_softmax: Optional[bool] = False,
        valid_sequence_lengths: Optional[torch.Tensor] = None,
        num_virtual_tokens: int = None,
        first_token: Optional[bool] = True,
        **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,
            token_idx=token_idx,
            reuse_cache=reuse_cache,
            cache_idx=cache_idx,
            cache_position=cache_position,
            attn_softmax_bf16=attn_softmax_bf16,
            use_flash_attention=use_flash_attention,
            flash_attention_recompute=flash_attention_recompute,
            flash_attention_causal_mask=flash_attention_causal_mask,
            flash_attention_fast_softmax=flash_attention_fast_softmax,
            valid_sequence_lengths=valid_sequence_lengths,
            num_virtual_tokens=num_virtual_tokens,
            first_token=first_token,
            **kwargs,
        )
        hidden_states = residual + hidden_states

        # Fully Connected
        residual = hidden_states
        hidden_states = self.post_attention_layernorm(hidden_states)
        if isinstance(self.mlp, DeepseekV2MoE):
            hidden_states = self.mlp(hidden_states)
        else:
            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 = False
    _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._attn_implementation = "eager"
        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 allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
        for layer in self.layers:
            layer.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len)

    def reorder_kv_cache(self, beam_idx: torch.LongTensor):
        return tuple(layer.reorder_kv_cache(beam_idx) for layer in self.layers)

    def update_sincos_cache(self, seq_len):
        for layer in self.layers:
            layer.update_sincos_cache(seq_len)

    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,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        token_idx: Optional[torch.Tensor] = None,
        attn_softmax_bf16: Optional[bool] = False,
        reuse_cache: Optional[bool] = False,
        use_flash_attention: Optional[bool] = False,
        flash_attention_recompute: Optional[bool] = False,
        flash_attention_causal_mask: Optional[bool] = False,
        flash_attention_fast_softmax: Optional[bool] = False,
        cache_idx: int = None,
        lazy_mode: Optional[bool] = True,
        valid_sequence_lengths: Optional[torch.Tensor] = None,
        num_virtual_tokens: int = None,
        first_token: Optional[bool] = True,
    ) -> 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

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

        ignore_cache_position = True  # Ignoring cache position for HPU
        use_new_cache = False  # Ignoring new Cache path for HPU

        past_seen_tokens = 0

        if past_key_values is not None and use_cache:  # kept for BC (cache positions)
            if reuse_cache:
                if isinstance(past_key_values[0][0], torch.Tensor):
                    past_seen_tokens = past_key_values[0][0].shape[2]
                else:
                    past_seen_tokens = past_key_values[0][0][2]
            else:
                if use_new_cache:
                    if not isinstance(past_key_values, StaticCache):
                        past_key_values = DynamicCache.from_legacy_cache(past_key_values)
                    past_seen_tokens = past_key_values.get_seq_length()
                else:
                    if past_key_values[0] is not None:  ##added for (None, None)
                        past_seen_tokens = past_key_values[0][0].shape[2]

        if ignore_cache_position is False:
            if cache_position is None:
                if isinstance(past_key_values, StaticCache):
                    raise ValueError("cache_position is a required argument when using StaticCache.")
                cache_position = torch.arange(
                    past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
                )
            if position_ids is None and cache_position:
                position_ids = cache_position.unsqueeze(0)

        else:
            if position_ids is None:
                position_ids = torch.arange(
                    past_seen_tokens, seq_length + past_seen_tokens, dtype=torch.long, device=inputs_embeds.device
                )
                position_ids = position_ids.unsqueeze(0)
            cache_position = None

        causal_mask = _gaudi_prepare_4d_causal_attention_mask(
            attention_mask,
            input_ids.shape if input_ids is not None else (batch_size, seq_length),
            inputs_embeds,
            past_seen_tokens,
        )

        # 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
        all_router_logits = () if output_router_logits else None
        next_decoder_cache = () if not use_new_cache else None

        if lazy_mode:
            htcore.mark_step()

        for layer_idx, decoder_layer in enumerate(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,
                    causal_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                    token_idx,
                    reuse_cache,
                    cache_idx,
                    cache_position,
                    attn_softmax_bf16,
                    use_flash_attention,
                    flash_attention_recompute,
                    flash_attention_causal_mask,
                    flash_attention_fast_softmax,
                    valid_sequence_lengths,
                    num_virtual_tokens,
                )
            else:
                if (
                    lazy_mode
                    and not self.training
                    and (torch.distributed.is_initialized() is False or torch.distributed.get_world_size() == 1)
                ):
                    htcore.mark_step()

                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_mask,
                    position_ids=position_ids,
                    past_key_value=None if past_key_values is None else past_key_values[layer_idx],
                    output_attentions=output_attentions,
                    output_router_logits=output_router_logits,
                    use_cache=use_cache,
                    cache_position=cache_position,
                    token_idx=token_idx,
                    attn_softmax_bf16=attn_softmax_bf16,
                    reuse_cache=reuse_cache,
                    use_flash_attention=use_flash_attention,
                    flash_attention_recompute=flash_attention_recompute,
                    flash_attention_causal_mask=flash_attention_causal_mask,
                    flash_attention_fast_softmax=flash_attention_fast_softmax,
                    cache_idx=cache_idx,
                    num_virtual_tokens=num_virtual_tokens,
                    first_token=first_token,
                )
                if (
                    lazy_mode
                    and not self.training
                    and (torch.distributed.is_initialized() is False or torch.distributed.get_world_size() == 1)
                ):
                    htcore.mark_step()

            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:
                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 isinstance(next_decoder_cache, 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,
        )


class DeepseekV2ForCausalLM(DeepseekV2PreTrainedModel, GenerationMixin):
    _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

    def allocate_kv_cache(self, batch_size, max_seq_len, inp_seq_len):
        self.model.allocate_kv_cache(batch_size, max_seq_len, inp_seq_len)
        self.kv_cache_len = max_seq_len

    def reorder_kv_cache(self, beam_idx: torch.LongTensor):
        return self.model.reorder_kv_cache(beam_idx)

    def update_sincos_cache(self, seq_len):
        self.model.update_sincos_cache(seq_len)

    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
        *model_args,
        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
        cache_dir: Optional[Union[str, os.PathLike]] = None,
        ignore_mismatched_sizes: bool = False,
        force_download: bool = False,
        local_files_only: bool = False,
        token: Optional[Union[str, bool]] = None,
        revision: str = "main",
        use_safetensors: bool = None,
        **kwargs,
    ):
        # Load config if we don't provide a configuration
        if not isinstance(config, PretrainedConfig):
            config_path = config if config is not None else pretrained_model_name_or_path
            config, model_kwargs = cls.config_class.from_pretrained(
                config_path,
                cache_dir=cache_dir,
                return_unused_kwargs=True,
                force_download=force_download,
                resume_download=False,
                proxies=None,
                local_files_only=local_files_only,
                token=token,
                revision=revision,
                subfolder="",
                _from_auto=False,
                _from_pipeline=None,
                **kwargs,
            )

        return super(DeepseekV2ForCausalLM, cls).from_pretrained(
            pretrained_model_name_or_path,
            *model_args,
            config=config,
            cache_dir=cache_dir,
            ignore_mismatched_sizes=ignore_mismatched_sizes,
            force_download=force_download,
            local_files_only=local_files_only,
            token=token,
            revision=revision,
            use_safetensors=use_safetensors,
            **kwargs,
        )

    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] = False,
        output_router_logits: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        token_idx: Optional[torch.Tensor] = None,
        reuse_cache: Optional[bool] = None,
        flash_attention_recompute: Optional[bool] = False,
        cache_idx: int = None,
        cache_position: Optional[torch.LongTensor] = None,
        trim_logits: Optional[bool] = False,
        attn_softmax_bf16: Optional[bool] = False,
        use_flash_attention: Optional[bool] = False,
        flash_attention_causal_mask: Optional[bool] = False,
        flash_attention_fast_softmax: Optional[bool] = False,
        valid_sequence_lengths: torch.Tensor = None,
        lazy_mode: Optional[bool] = True,
        num_virtual_tokens: int = None,
        first_token: Optional[bool] = True,
    ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
        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,
            output_router_logits=output_router_logits,
            return_dict=return_dict,
            cache_position=cache_position,
            token_idx=token_idx,
            attn_softmax_bf16=attn_softmax_bf16,
            reuse_cache=reuse_cache,
            use_flash_attention=use_flash_attention,
            flash_attention_recompute=flash_attention_recompute,
            flash_attention_causal_mask=flash_attention_causal_mask,
            flash_attention_fast_softmax=flash_attention_fast_softmax,
            cache_idx=cache_idx,
            lazy_mode=lazy_mode,
            valid_sequence_lengths=valid_sequence_lengths,
            num_virtual_tokens=num_virtual_tokens,
            first_token=first_token,
        )

        hidden_states = outputs[0]

        _, seq_len, _ = hidden_states.shape
        if seq_len > 1 and trim_logits and not self.training:
            if token_idx is not None:
                hidden_states = hidden_states.index_select(1, token_idx - 1)
            else:
                hidden_states = hidden_states[:, -1, :]

        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,
        position_ids=None,
        use_cache=True,
        num_logits_to_keep=None,
        token_idx=None,
        **kwargs,
    ):
        reuse_cache = kwargs.get("reuse_cache")
        bucket_internal = kwargs.get("bucket_internal")

        if past_key_values is not None:
            if token_idx is not None:
                idx = token_idx + kwargs.get("inputs_embeds_offset", 0) - 1
                input_ids = torch.index_select(input_ids, 1, idx)
            else:
                if inputs_embeds is not None:  # Exception 1
                    input_ids = input_ids[:, -cache_position.shape[0] :]
                elif (
                    input_ids.shape[1] != cache_position.shape[0]
                ):  # Default case (the "else", a no op, is Exception 2)
                    input_ids = input_ids[:, cache_position]
        elif (reuse_cache or bucket_internal) and token_idx is not None:
            # KV cache is pre allocated with reuse cache or will be padded with bucket internal
            # hence for the 1st token we can slice the inputs till token idx for the fwd pass.
            input_ids = input_ids[:, :token_idx]
            attention_mask = attention_mask[:, :token_idx]

        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:
                if token_idx is not None:
                    position_ids = torch.index_select(position_ids, 1, token_idx - 1)
                else:
                    position_ids = position_ids[:, -input_ids.shape[1] :]

        # keep cache_position implementation as None for HPU
        cache_position = None

        # 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.contiguous()}

        if num_logits_to_keep is not None:
            model_inputs["num_logits_to_keep"] = num_logits_to_keep

        model_inputs.update(
            {
                "position_ids": position_ids,
                "cache_position": cache_position,
                "past_key_values": past_key_values,
                "use_cache": use_cache,
                "attention_mask": attention_mask,
                "token_idx": token_idx,
                "trim_logits": kwargs.get("trim_logits"),
                "attn_softmax_bf16": kwargs.get("attn_softmax_bf16"),
                "reuse_cache": reuse_cache,
                "use_flash_attention": kwargs.get("use_flash_attention"),
                "flash_attention_recompute": kwargs.get("flash_attention_recompute"),
                "flash_attention_causal_mask": kwargs.get("flash_attention_causal_mask"),
                "flash_attention_fast_softmax": kwargs.get("flash_attention_fast_softmax"),
                "valid_sequence_lengths": kwargs.get("valid_sequence_lengths"),
                "cache_idx": kwargs.get("cache_idx"),
                "lazy_mode": kwargs.get("lazy_mode"),
                "num_virtual_tokens": kwargs.get("num_virtual_tokens"),
                "first_token": kwargs.get("first_token", True),
            }
        )
        return model_inputs


@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,
        )