modeling_jetmoe.py

""" PyTorch JetMoE model."""

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

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
from torch.nn import functional as F

from transformers.modeling_outputs import (
    BaseModelOutputWithPast, 
    CausalLMOutputWithPast,
    SequenceClassifierOutputWithPast,
    dataclass
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import (
    add_start_docstrings, 
    add_start_docstrings_to_model_forward,
    is_flash_attn_2_available,
    is_flash_attn_greater_or_equal_2_10,
    replace_return_docstrings, 
    logging
)
from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa
from transformers.cache_utils import Cache, DynamicCache
from .configuration_jetmoe import JetMoEConfig
import scattermoe

try:
    if is_flash_attn_2_available():
        from flash_attn import flash_attn_func, flash_attn_varlen_func
        from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
except ImportError:
    # Workaround for https://github.com/huggingface/transformers/issues/28459,
    # don't move to contextlib.suppress(ImportError)
    pass

logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "jetmoe"
_CONFIG_FOR_DOC = "JetMoEConfig"

class top_k_gating(nn.Module):
    def __init__(
        self,
        input_size, 
        num_experts, 
        top_k,
    ):
        """
        Initialize the top-k gating mechanism.

        Args:
            input_size (int): Size of the input.
            num_experts (int): Number of experts.
            top_k (int): Number of top experts to select.
            acc_aux_loss (bool): Whether to accumulate auxiliary loss statistics.
            dropout (float): Dropout rate for gating network.
            hidden_size (int): Hidden size of the gating network.
            sample_topk (int): Number of top-k experts to sample during training.
            aux_loss (str): Type of auxiliary loss ('mi' or 'switch').
            gate_type (str): Type of gating mechanism ('mlp', 'linear', or 'gmm').
        """
        super().__init__()

        self.num_experts = num_experts
        self.input_size = input_size
        assert top_k <= num_experts
        self.top_k = top_k

        self.layer = nn.Linear(input_size, num_experts, bias=False)

    def extra_repr(self):
        """
        Return extra representation string for the module.
        """
        return 'k={}, num_experts={}'.format(
            self.top_k, self.num_experts)

    def compute_aux_loss(self, probs, logits, gates):
        """
        Calculate and return the auxiliary loss based on the accumulated statistics.

        Args:
            eps (float): Small epsilon value for numerical stability.

        Returns:
            torch.Tensor: The calculated auxiliary loss.
        """
        count = logits.size(0)
        probs = probs.sum(0)
        freq = (gates > 0).float().sum(0)
        lsesq = (torch.log(torch.exp(logits).sum(dim=-1)) ** 2).sum()

        switchloss =  self.num_experts * (
            F.normalize(probs, p=1, dim=0) *
            F.normalize(freq, p=1, dim=0)
        ).sum()
        zloss = lsesq / count
        loss = switchloss + 0.1 * zloss

        return loss

    def forward(self, x):
        """
        Compute the top-k gating for the input.

        See paper: https://arxiv.org/abs/1701.06538.

        Args:
            x (torch.Tensor): Input tensor with shape [batch_size, input_size].
            skip_mask (torch.Tensor): Skip mask tensor (binary) with the same shape as `x`.
            x: input Tensor with shape [batch_size, input_size]
            train: a boolean - we only add noise at training time.
            noise_epsilon: a float

        Returns:
            torch.Tensor: Top-k indices.
            torch.Tensor: Top-k gating values.
            torch.Tensor: Probability values for each expert.
            gates: a Tensor with shape [batch_size, num_experts]
            load: a Tensor with shape [num_experts]
        """

        logits = self.layer(x).float()
        top_k_logits, top_k_indices = logits.topk(self.top_k, dim=1)
        top_k_gates = torch.softmax(top_k_logits, dim=1).type_as(x)

        if self.training:
            probs = torch.softmax(logits, dim=1)
            zeros = torch.zeros_like(probs)
            zeros = zeros.to(top_k_gates.dtype)  # Convert zeros to match top_k_gates dtype
            gates = zeros.scatter(1, top_k_indices, top_k_gates)
            self.loss = self.compute_aux_loss(probs, logits, gates)
        else:
            self.loss = 0

        return top_k_indices, top_k_gates

class MoE(nn.Module):
    """
    A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
    

    Args:
        input_size: integer - size of the input
        head_size: integer - size of the expert's hidden layer
        num_experts: an integer - number of experts
        top_k: an integer - how many experts to use for each batch element
        bias: a boolean - whether to include bias in linear layers
        activation: an activation function to apply to expert's outputs
        acc_aux_loss: a boolean - whether to accumulate auxiliary loss
        hidden_size: an integer - hidden size of the experts
        gating_dropout: a float - dropout rate for gating network
        sample_topk: an integer - how many experts to sample during training
        gating_size: an integer - size of the gating network
        aux_loss: a string - type of auxiliary loss ('mi' or 'sparse')
        gate_type: a string - type of gating mechanism ('mlp' or 'topk')
    """

    def __init__(
        self, 
        input_size, 
        hidden_size, 
        num_experts, 
        top_k,
        bias=True, 
        activation=None, 
        glu=True,
        ):
        super(MoE, self).__init__()

        self.num_experts = num_experts
        self.input_size = input_size
        self.glu = glu
        if bias:
            self.bias = torch.nn.Parameter(torch.empty(input_size))
            torch.nn.init.zeros_(self.bias)
        else:
            self.bias = None
        self.input_linear = scattermoe.parallel_experts.ParallelExperts(num_experts, input_size, hidden_size * 2 if glu else hidden_size)
        self.output_linear = scattermoe.parallel_experts.ParallelExperts(num_experts, hidden_size, input_size)
        self.top_k = min(top_k, self.num_experts)
        self.activation = activation

        self.router = top_k_gating(
            input_size=input_size, 
            num_experts=num_experts, 
            top_k=top_k, 
            )

    def extra_repr(self):
        return 'k={}, e={}'.format(
            self.top_k, self.num_experts)

    def get_aux_loss_and_clear(self):
        """
        Get the accumulated auxiliary loss and clear it.

        Returns:
            float: Accumulated auxiliary loss.
        """

        return self.gate.get_aux_loss_and_clear()
    
    def compute_gate(self, x):
        top_k_indices, self.top_k_gates = self.router(x)

        with torch.no_grad():
            self.sorted_expert_idxs, self.sorted_scattered_idxs = scattermoe.kernels.ops.flatten_and_sort(top_k_indices)
            self.padded_block_idxs, self.expert_offsets = scattermoe.kernels.ops.padded_block_indices(self.sorted_expert_idxs, self.num_experts)

        return self.router.loss

    def batch_forward(self, x):
        """
        Forward pass of the mixture of experts layer.

        Args:
            x (Tensor): Input tensor.

        Returns:
            Tensor: Output tensor.
        """
        bsz, length, emb_size = x.size()
        x = x.reshape(-1, emb_size)

        loss = self.compute_gate(x)

        h = self.input_linear(
            x, self.top_k,
            self.sorted_expert_idxs, self.sorted_scattered_idxs,
            self.padded_block_idxs, self.expert_offsets,
            grouped_out=True
        )

        if self.glu:
            h, g = h.chunk(2, dim=-1)
            h = self.activation(h) * g
        else:
            h = self.activation(h)

        y = self.output_linear(
            h, 1, 
            self.sorted_expert_idxs, self.sorted_scattered_idxs,
            self.padded_block_idxs, self.expert_offsets,
            grouped_in=True,
            gates=self.top_k_gates,
        )

        y = y.view(bsz, length, self.input_size)
        if self.bias is not None:
            y = y + self.bias
        return y, loss
    
    def single_forward(self, x):
        bsz, length, emb_size = x.size()

        x = x.reshape(1, self.input_size)
        top_k_indices, top_k_gates = self.router(x)
        loss = self.router.loss

        y_list = []
        for i in range(self.top_k):
            expert_idx = top_k_indices[0,i]

            h = F.linear(x, self.input_linear.weight[expert_idx])
            if self.glu:
                h, g = h.chunk(2, dim=-1)
                h = self.activation(h) * g
            else:
                h = self.activation(h)
            y = F.linear(h, self.output_linear.weight[expert_idx]) * top_k_gates[0,i]

            y_list.append(y)
        
        y = sum(y_list)
        y = y.view(bsz, length, self.input_size)
        if self.bias is not None:
            y = y + self.bias
        return y, loss
    
    def forward(self, x):
        """
        Forward pass of the mixture of experts layer.

        Args:
            x (Tensor): Input tensor.

        Returns:
            Tensor: Output tensor.
        """
        bsz, length, emb_size = x.size()
        if bsz * length ==1:
            return self.single_forward(x)
        else:
            return self.batch_forward(x)

    def batch_map(self, x):
        """
        Map input through the mixture of experts layer.

        Args:
            x (Tensor): Input tensor.

        Returns:
            Tensor: Output tensor.
        """
        bsz, length, emb_size = x.size()
        x = x.reshape(-1, emb_size)
        loss = self.compute_gate(x)

        y = self.input_linear(
            x, self.top_k,
            self.sorted_expert_idxs, self.sorted_scattered_idxs,
            self.padded_block_idxs, self.expert_offsets,
        )
        y = y.view(bsz, length, self.top_k, -1)
        return y, loss
    
    def single_map(self, x):
        bsz, length, emb_size = x.size()

        x = x.reshape(1, self.input_size)
        self.top_k_indices, self.top_k_gates = self.router(x)
        loss = self.router.loss

        y_list = []
        for i in range(self.top_k):
            expert_idx = self.top_k_indices[0,i]
            y = F.linear(x, self.input_linear.weight[expert_idx])
            y_list.append(y)
        y = torch.cat(y_list, dim=0)
        y = y.view(bsz, length, self.top_k, -1)
        return y, loss
    
    def map(self, x):
        """
        Map input through the mixture of experts layer.

        Args:
            x (Tensor): Input tensor.

        Returns:
            Tensor: Output tensor.
        """
        bsz, length, emb_size = x.size()
        if bsz * length ==1:
            return self.single_map(x)
        else:
            return self.batch_map(x)

    def batch_reduce(self, x):
        """
        Reduce the mapped output.

        Args:
            x (Tensor): Mapped output tensor.

        Returns:
            Tensor: Reduced output tensor.
        """
        
        bsz, length, k, emb_size = x.size()
        assert k == self.top_k
        x = x.reshape(-1, emb_size)

        y = self.output_linear(
            x, 1, 
            self.sorted_expert_idxs, self.sorted_scattered_idxs,
            self.padded_block_idxs, self.expert_offsets,
            gates=self.top_k_gates,
        )
        y = y.view(bsz, length, self.input_size)
        return y
    
    def single_reduce(self, x):
        bsz, length, k, emb_size = x.size()

        x = x.reshape(k, emb_size)

        y_list = []
        for i in range(self.top_k):
            expert_idx = self.top_k_indices[0,i]
            y = F.linear(x[i], self.output_linear.weight[expert_idx]) * self.top_k_gates[0,i]
            y_list.append(y)
        y = sum(y_list)
        y = y.view(bsz, length, self.input_size)
        return y
    
    def reduce(self, x):
        """
        Reduce the mapped output.

        Args:
            x (Tensor): Mapped output tensor.

        Returns:
            Tensor: Reduced output tensor.
        """
        bsz, length, k, emb_size = x.size()
        if bsz * length ==1:
            return self.single_reduce(x)
        else:
            return self.batch_reduce(x)

@dataclass
class JetMoEBaseModelOutputWithPast(BaseModelOutputWithPast):
    """
    Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).

    Args:
        last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
            Sequence of hidden-states at the output of the last layer of the model.

            If `past_key_values` is used only the last hidden-state of the sequences of shape `(batch_size, 1,
            hidden_size)` is output.
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            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)`) and optionally if
            `config.is_encoder_decoder=True` 2 additional tensors of shape `(batch_size, num_heads,
            encoder_sequence_length, embed_size_per_head)`.

            Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
            `config.is_encoder_decoder=True` in the cross-attention blocks) that can be used (see `past_key_values`
            input) to speed up sequential decoding.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    last_hidden_state: torch.FloatTensor = None
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    aux_loss: Optional[torch.FloatTensor] = None


@dataclass
class JetMoECausalLMOutputWithPast(CausalLMOutputWithPast):
    """
    Base class for causal language model (or autoregressive) outputs.

    Args:
        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Language modeling loss (for next-token prediction).
        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            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)`)

            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
            `past_key_values` input) to speed up sequential decoding.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    aux_loss: Optional[torch.FloatTensor] = None


@dataclass
class JetMoESequenceClassifierOutputWithPast(SequenceClassifierOutputWithPast):
    """
    Base class for outputs of sentence classification models.

    Args:
        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
            Classification (or regression if config.num_labels==1) loss.
        logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
            Classification (or regression if config.num_labels==1) scores (before SoftMax).
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            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)`)

            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
            `past_key_values` input) to speed up sequential decoding.
        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    loss: Optional[torch.FloatTensor] = None
    logits: torch.FloatTensor = None
    past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
    hidden_states: Optional[Tuple[torch.FloatTensor]] = None
    attentions: Optional[Tuple[torch.FloatTensor]] = None
    aux_loss: Optional[torch.FloatTensor] = None


# Copied from transformers.models.llama.modeling_llama._get_unpad_data
def _get_unpad_data(attention_mask):
    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
    max_seqlen_in_batch = seqlens_in_batch.max().item()
    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
    return (
        indices,
        cu_seqlens,
        max_seqlen_in_batch,
    )

class JetMoERMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        """
        JetMoERMSNorm module
        """
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)


# copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding
class JetMoERotaryEmbedding(nn.Module):
    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
        super().__init__()

        self.dim = dim
        self.max_position_embeddings = max_position_embeddings
        self.base = base
        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
        self.register_buffer("inv_freq", inv_freq, persistent=False)

        # Build here to make `torch.jit.trace` work.
        self._set_cos_sin_cache(
            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
        )

    def _set_cos_sin_cache(self, seq_len, device, dtype):
        self.max_seq_len_cached = seq_len
        t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)

        freqs = torch.outer(t, self.inv_freq)
        # Different from paper, but it uses a different permutation in order to obtain the same calculation
        emb = torch.cat((freqs, freqs), dim=-1)
        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)

    def forward(self, x, seq_len=None):
        # x: [bs, num_attention_heads, seq_len, head_size]
        if seq_len > self.max_seq_len_cached:
            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)

        return (
            self.cos_cached[:seq_len].to(dtype=x.dtype),
            self.sin_cached[:seq_len].to(dtype=x.dtype),
        )


# Copied from transformers.models.llama.modeling_llama.rotate_half
def rotate_half(x):
    """Rotates half the hidden dims of the input."""
    x1 = x[..., : x.shape[-1] // 2]
    x2 = x[..., x.shape[-1] // 2 :]
    return torch.cat((-x2, x1), dim=-1)


# copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=2):
    """Applies Rotary Position Embedding to the query and key tensors.

    Args:
        q (`torch.Tensor`): The query tensor.
        k (`torch.Tensor`): The key tensor.
        cos (`torch.Tensor`): The cosine part of the rotary embedding.
        sin (`torch.Tensor`): The sine part of the rotary embedding.
        position_ids (`torch.Tensor`):
            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
            used to pass offsetted position ids when working with a KV-cache.
        unsqueeze_dim (`int`, *optional*, defaults to 1):
            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
    Returns:
        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
    """
    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
    q_embed = (q * cos) + (rotate_half(q) * sin)
    k_embed = (k * cos) + (rotate_half(k) * sin)
    return q_embed, k_embed


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

    def __init__(self, config: JetMoEConfig, layer_idx: Optional[int] = None):
        """
        Initialize the JetMoEAttention module.

        Args:
            config: Configuration object with model hyperparameters.
        """
        super().__init__()
        self.config = config
        self.layer_idx = layer_idx
        self.is_causal = True
        if layer_idx is None:
            logger.warning_once(
                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
                "when creating this class."
            )

        self.top_k = config.moe_top_k

        self.kv_projection_size = config.kv_channels * config.num_attention_heads
        self.num_key_value_heads = config.num_attention_heads
        self.num_heads = self.num_key_value_heads * self.top_k
        self.hidden_size_per_attention_head = config.kv_channels

        self.experts = MoE(
            input_size=config.hidden_size,
            hidden_size=self.kv_projection_size,
            num_experts=config.moe_num_experts,
            top_k=config.moe_top_k,
            glu=False
        )

        self.kv_proj = torch.nn.Linear(
            config.hidden_size, self.kv_projection_size * 2, bias=False
            )
        
        self.rotary_emb = JetMoERotaryEmbedding(
            config.kv_channels,
            max_position_embeddings=config.max_position_embeddings,
            base=config.rope_theta,
        )

    # def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
    #     return tensor.view(bsz, seq_len, self.num_attention_heads, self.hidden_size_per_attention_head).transpose(1, 2).contiguous()

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
        **kwargs,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if "padding_mask" in kwargs:
            warnings.warn(
                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
            )
        bsz, q_len, _ = hidden_states.size()

        query_states, aux_loss = self.experts.map(hidden_states)
        key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.hidden_size_per_attention_head).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.hidden_size_per_attention_head).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.hidden_size_per_attention_head).transpose(1, 2)

        kv_seq_len = key_states.shape[2]
        if past_key_value is not None:
            if self.layer_idx is None:
                raise ValueError(
                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
                    "with a layer index."
                )
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids, unsqueeze_dim=1)

        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)

        # repeat k/v heads if n_kv_heads < n_heads
        key_states = key_states.repeat(1, self.top_k, 1, 1)
        value_states = value_states.repeat(1, self.top_k, 1, 1)

        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.hidden_size_per_attention_head)

        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
            raise ValueError(
                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
                f" {attn_weights.size()}"
            )

        if attention_mask is not None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
                )

            attn_weights = attn_weights + attention_mask

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

        if attn_output.size() != (bsz, self.num_heads, q_len, self.hidden_size_per_attention_head):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.hidden_size_per_attention_head)}, but is"
                f" {attn_output.size()}"
            )

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)

        attn_output = self.experts.reduce(attn_output)
        attn_output = attn_output.view(bsz, q_len, -1)

        if not output_attentions:
            attn_weights = None

        return attn_output, attn_weights, past_key_value, aux_loss
    

# copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->JetMoE
class JetMoESdpaAttention(JetMoEAttention):
    """
    JetMoE attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
    `JetMoEAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
    SDPA API.
    """

    # Adapted from JetMoEAttention.forward
    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        if output_attentions:
            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
            logger.warning_once(
                "JetMoEModel is using JetMoESdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
            )
            return super().forward(
                hidden_states=hidden_states,
                attention_mask=attention_mask,
                position_ids=position_ids,
                past_key_value=past_key_value,
                output_attentions=output_attentions,
                use_cache=use_cache,
            )

        bsz, q_len, _ = hidden_states.size()

        query_states, aux_loss = self.experts.map(hidden_states)
        key_states, value_states = self.kv_proj(hidden_states).chunk(2, dim=-1)

        query_states = query_states.view(bsz, q_len, self.num_heads, self.hidden_size_per_attention_head).transpose(1, 2)
        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.hidden_size_per_attention_head).transpose(1, 2)
        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.hidden_size_per_attention_head).transpose(1, 2)

        kv_seq_len = key_states.shape[2]
        if past_key_value is not None:
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)

        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids, unsqueeze_dim=1)

        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)

        key_states = key_states.repeat(1, self.top_k, 1, 1)
        value_states = value_states.repeat(1, self.top_k, 1, 1)

        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()}"
                )

        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
        # Reference: https://github.com/pytorch/pytorch/issues/112577.
        if query_states.device.type == "cuda" and attention_mask is not None:
            query_states = query_states.contiguous()
            key_states = key_states.contiguous()
            value_states = value_states.contiguous()

        attn_output = torch.nn.functional.scaled_dot_product_attention(
            query_states,
            key_states,
            value_states,
            attn_mask=attention_mask,
            dropout_p=0.0,
            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
            is_causal=self.is_causal and attention_mask is None and q_len > 1,
        )

        attn_output = attn_output.transpose(1, 2).contiguous()
        attn_output = attn_output.reshape(bsz, q_len, self.top_k, self.kv_projection_size)

        attn_output = self.experts.reduce(attn_output)
        attn_output = attn_output.view(bsz, q_len, -1)

        return attn_output, None, past_key_value, aux_loss


class JetMoEFlashAttention2(JetMoEAttention):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()

    def forward(
        self,
        hidden_states: Optional[torch.FloatTensor],
        attention_mask: Optional[torch.FloatTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Cache] = None,
        use_cache: Optional[bool] = False,
        output_attentions: Optional[bool] = False,
        **kwargs,
    ) -> Union[
        Tuple[torch.Tensor, Tuple[torch.Tensor]],
        Optional[Tuple[torch.Tensor, Tuple[torch.Tensor], Tuple[torch.Tensor, ...]]],
    ]:
        """
        Forward pass of the JetMoEAttention module.

        Args:
            hidden_states (Optional[torch.FloatTensor]): Input hidden states.
            attention_mask (Optional[torch.FloatTensor]): Attention mask.
            layer_past (Optional[Tuple[torch.Tensor]]): Past layer state.
            use_cache (Optional[bool]): Whether to use cached states.
            output_attentions (Optional[bool]): Whether to output attention weights.

        Returns:
            Union[Tuple[torch.Tensor, Tuple[torch.Tensor]], Optional[Tuple[...]]]: Tuple containing outputs.
        """
        #assert attention_mask is None, "attention_mask is not supported"
        assert output_attentions is False, "output_attentions is not supported"

        B, T, C = hidden_states.size() # batch size, sequence length, embedding dimensionality (hidden_size)

        # calculate query, key, values 
        query_layer, aux_loss = self.experts.map(hidden_states)
        key_layer, value_layer = self.kv_proj(hidden_states).chunk(2, dim=-1)

        query_layer = query_layer.view(B, T, self.num_heads, self.hidden_size_per_attention_head) # (B, T, k * nh, hs)
        key_layer = key_layer.view(B, T, self.num_key_value_heads, self.hidden_size_per_attention_head) # (B, T, nh, hs)
        value_layer = value_layer.view(B, T, self.num_key_value_heads, self.hidden_size_per_attention_head) # (B, T, nh, hs)

        kv_seq_len = key_layer.shape[1]
        if past_key_value is not None:
            if self.layer_idx is None:
                raise ValueError(
                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
                    "with a layer index."
                )
            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
        cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len)
        query_layer, key_layer = apply_rotary_pos_emb(query_layer, key_layer, cos, sin, position_ids)

        # query_layer = query_layer.contiguous()
        # expand the key_layer and value_layer [sk, b, ng, hn] -> [sk, b, np, hn]
        key_layer = key_layer.repeat(1, 1, self.top_k, 1)
        value_layer = value_layer.repeat(1, 1, self.top_k, 1)

        if past_key_value is not None:
            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
            # print(self.layer_idx, key_layer.size())
            key_layer = key_layer.transpose(1, 2)
            value_layer = value_layer.transpose(1, 2)
            key_layer, value_layer = past_key_value.update(key_layer, value_layer, self.layer_idx, cache_kwargs)
            key_layer = key_layer.transpose(1, 2)
            value_layer = value_layer.transpose(1, 2)

        context_layer = self._flash_attention_forward(
            query_layer,
            key_layer,
            value_layer,
            attention_mask,
            T,
        ) 

        # output projection
        y = self.experts.reduce(context_layer.reshape(T, B, self.top_k, self.kv_projection_size))
        y = y.view(B, T, C) # re-assemble all head outputs side by side

        if not output_attentions:
            attn_weights = None

        return y, attn_weights, past_key_value, aux_loss

    def _flash_attention_forward(
        self,
        query_states,
        key_states,
        value_states,
        attention_mask,
        query_length,
        dropout=0.0,
        softmax_scale=None,
    ):
        """
        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
        first unpad the input, then computes the attention scores and pad the final attention scores.

        Args:
            query_states (`torch.Tensor`):
                Input query states to be passed to Flash Attention API
            key_states (`torch.Tensor`):
                Input key states to be passed to Flash Attention API
            value_states (`torch.Tensor`):
                Input value states to be passed to Flash Attention API
            attention_mask (`torch.Tensor`):
                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
                position of padding tokens and 1 for the position of non-padding tokens.
            dropout (`float`):
                Attention dropout
            softmax_scale (`float`, *optional*):
                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
        """
        if not self._flash_attn_uses_top_left_mask:
            causal = self.is_causal
        else:
            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
            causal = self.is_causal and query_length != 1

        # Contains at least one padding token in the sequence
        if attention_mask is not None:
            batch_size = query_states.shape[0]
            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
                query_states, key_states, value_states, attention_mask, query_length
            )

            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens

            attn_output_unpad = flash_attn_varlen_func(
                query_states,
                key_states,
                value_states,
                cu_seqlens_q=cu_seqlens_q,
                cu_seqlens_k=cu_seqlens_k,
                max_seqlen_q=max_seqlen_in_batch_q,
                max_seqlen_k=max_seqlen_in_batch_k,
                dropout_p=dropout,
                softmax_scale=softmax_scale,
                causal=causal,
            )

            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
        else:
            attn_output = flash_attn_func(
                query_states,
                key_states,
                value_states,
                dropout,
                softmax_scale=softmax_scale,
                causal=causal
            )

        return attn_output


    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape

        key_layer = index_first_axis(
            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )
        value_layer = index_first_axis(
            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
        )
        if query_length == kv_seq_len:
            query_layer = index_first_axis(
                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
            )
            cu_seqlens_q = cu_seqlens_k
            max_seqlen_in_batch_q = max_seqlen_in_batch_k
            indices_q = indices_k
        elif query_length == 1:
            max_seqlen_in_batch_q = 1
            cu_seqlens_q = torch.arange(
                batch_size + 1, dtype=torch.int32, device=query_layer.device
            )  # There is a memcpy here, that is very bad.
            indices_q = cu_seqlens_q[:-1]
            query_layer = query_layer.squeeze(1)
        else:
            # The -q_len: slice assumes left padding.
            attention_mask = attention_mask[:, -query_length:]
            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)

        return (
            query_layer,
            key_layer,
            value_layer,
            indices_q,
            (cu_seqlens_q, cu_seqlens_k),
            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
        )


JETMOE_ATTENTION_CLASSES = {
    "eager": JetMoEAttention,
    "flash_attention_2": JetMoEFlashAttention2,
    "sdpa": JetMoESdpaAttention,
}


class JetMoEBlock(nn.Module):
    def __init__(self, config: JetMoEConfig, layer_idx: Optional[int] = None):
        """
        Initialize the JetMoEBlock module.

        Args:
            config: Configuration object with model hyperparameters.
        """
        super().__init__()
        self.input_layernorm = JetMoERMSNorm(config.hidden_size)
        #self.self_attention = JetMoEAttention(config, layer_idx)
        self.self_attention = JETMOE_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
        self.post_attention_layernorm = JetMoERMSNorm(config.hidden_size)

        # moe_args = megablocks.layers.arguments.from_megatron(config)
        # moe_args.activation_fn = F.silu
        # moe_args.return_bias = False
        # self.mlp = megablocks.layers.dmoe.dMoE(moe_args)
        self.mlp = MoE(
            input_size=config.hidden_size,
            hidden_size=config.ffn_hidden_size,
            num_experts=config.moe_num_experts,
            activation=F.silu,
            top_k=config.moe_top_k,
            glu=config.glu
        )

    def forward(
        self,
        hidden_states: Optional[torch.FloatTensor],
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = False,
        use_cache: Optional[bool] = False,
        **kwargs,
    ) -> Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
        """
        Forward pass of the JetMoEBlock module.

        Args:
            hidden_states (Optional[torch.FloatTensor]): Input hidden states.
            layer_past (Optional[Tuple[torch.Tensor]]): Past layer state.
            attention_mask (Optional[torch.FloatTensor]): Attention mask.
            head_mask (Optional[torch.FloatTensor]): Head mask.
            use_cache (Optional[bool]): Whether to use cached states.
            output_attentions (Optional[bool]): Whether to output attention weights.

        Returns:
            Union[Tuple[torch.Tensor], Optional[Tuple[torch.Tensor, Tuple[torch.FloatTensor, ...]]]]:
            Tuple containing outputs or optional attention weights.
        """
        # Self Attention
        attn_output, self_attn_weights, present_key_value, att_aux_loss = self.self_attention(
            hidden_states=self.input_layernorm(hidden_states),
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_value=past_key_value,
            output_attentions=output_attentions,
            use_cache=use_cache,
        )

        hidden_states = hidden_states + attn_output
        x_mlp, mlp_aux_loss = self.mlp(self.post_attention_layernorm(hidden_states))
        hidden_states = hidden_states + x_mlp

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (self_attn_weights,)

        if use_cache:
            outputs += (present_key_value,)

        outputs += (att_aux_loss + mlp_aux_loss,)

        return outputs



class JetMoEPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = JetMoEConfig
    base_model_prefix = "transformer"
    supports_gradient_checkpointing = True
    _no_split_modules = ["JetMoEBlock"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True

    def __init__(self, *inputs, **kwargs):
        """
        Initialize the JetMoEPreTrainedModel.

        Args:
            *inputs: Variable length input arguments.
            **kwargs: Keyword arguments.
        """
        super().__init__(*inputs, **kwargs)

        self.gradient_checkpointing = False

    def _init_weights(self, module):
        """Initialize the weights."""
        if isinstance(module, (nn.Linear,)):
            # Slightly different from Mesh Transformer JAX which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)

    # def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs={}):
    #     for module in self.modules():
    #         if hasattr(module, "gradient_checkpointing"):
    #             self._set_gradient_checkpointing(
    #                 module, True, gradient_checkpointing_kwargs
    #             )

    # def gradient_checkpointing_disable(self):
    #     for module in self.modules():
    #         if hasattr(module, "gradient_checkpointing"):
    #             self._set_gradient_checkpointing(
    #                 module, False
    #             )

    # def _set_gradient_checkpointing(
    #     self,
    #     module,
    #     value=False,
    #     gradient_checkpointing_kwargs={"use_reentrant": False},
    # ):
    #     """
    #     Set gradient checkpointing for the JetMoEModel.

    #     Args:
    #         module: The module for which gradient checkpointing is set.
    #         value (bool): Whether to enable gradient checkpointing.
    #     """
    #     self._gradient_checkpointing_func = checkpoint
    #     self.gradient_checkpointing = True
    #     if isinstance(module, JetMoEModel):
    #         module.gradient_checkpointing = value
    #         module.gradient_checkpointing_kwargs = gradient_checkpointing_kwargs
    #         module._gradient_checkpointing_func = checkpoint

MODULEFORMER_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`JetMoEConfig`]): 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.
"""

MODULEFORMER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoProcenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `({0})`, *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)
        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`torch.LongTensor` of shape `({0})`, *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)
        head_mask (`torch.FloatTensor` of shape `(num_attention_heads,)` or `(n_layer, num_attention_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_dim)`, *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.
        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 JetMoE Model outputting raw hidden-states without any specific head on top.",
    MODULEFORMER_START_DOCSTRING,
)
class JetMoEModel(JetMoEPreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`JetMoEBlock`]

    Args:
        config: JetMoEConfig
    """

    def __init__(self, config: JetMoEConfig):
        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(
            [JetMoEBlock(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self._attn_implementation = config._attn_implementation
        self.norm = JetMoERMSNorm(config.hidden_size, eps=config.rms_norm_eps)

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

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value

    @add_start_docstrings_to_model_forward(MODULEFORMER_INPUTS_DOCSTRING)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")

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

        past_key_values_length = 0

        if use_cache:
            use_legacy_cache = not isinstance(past_key_values, Cache)
            if use_legacy_cache:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
            past_key_values_length = past_key_values.get_usable_length(seq_length)

        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(
                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
            )
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
        else:
            position_ids = position_ids.view(-1, seq_length).long()

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

        if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
            if is_padding_right:
                raise ValueError(
                    "You are attempting to perform batched generation with padding_side='right'"
                    " this may lead to unexpected behaviour for Flash Attention version of JetMoE. Make sure to "
                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. "
                )

        if self._attn_implementation == "flash_attention_2":
            # 2d mask is passed through the layers
            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
        elif self._attn_implementation == "sdpa" and not output_attentions:
            # output_attentions=True can not be supported when using SDPA, and we fall back on
            # the manual implementation that requires a 4D causal mask in all cases.
            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
            )
        else:
            # 4d mask is passed through the layers
            attention_mask = _prepare_4d_causal_attention_mask(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
            )

        hidden_states = inputs_embeds

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None

        aux_loss = 0
        for decoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            # hidden_states: Optional[torch.FloatTensor],
            # position_ids: Optional[torch.LongTensor] = None,
            # past_key_value: Optional[Tuple[torch.Tensor]] = None,
            # attention_mask: Optional[torch.FloatTensor] = None,
            # output_attentions: Optional[bool] = False,
            # use_cache: Optional[bool] = False,

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    #decoder_layer.__call__,
                    decoder_layer,
                    hidden_states,
                    position_ids,
                    past_key_values,
                    attention_mask,
                    output_attentions,
                    use_cache,
                    use_reentrant=False,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

            aux_loss += layer_outputs[-1]

        hidden_states = self.norm(hidden_states)

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

        next_cache = None
        if use_cache:
            next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache

        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return JetMoEBaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
            aux_loss=aux_loss,
        )


class JetMoEForCausalLM(JetMoEPreTrainedModel):
    _tied_weights_keys = ["lm_head.weight"]

    def __init__(self, config):
        super().__init__(config)
        self.model = JetMoEModel(config)
        self.vocab_size = config.vocab_size
        self.aux_loss_coef = getattr(config, 'aux_loss_coef', 0.01)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

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

    def get_input_embeddings(self):
        return self.model.embed_tokens

    def set_input_embeddings(self, value):
        self.model.embed_tokens = value

    def get_output_embeddings(self):
        return self.lm_head

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def set_decoder(self, decoder):
        self.model = decoder

    def get_decoder(self):
        return self.model

    @add_start_docstrings_to_model_forward(MODULEFORMER_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, CausalLMOutputWithPast]:
        r"""
        Args:
            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                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, ..., config.vocab_size]`.

        Returns:
        """

        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = self.model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)
        logits = logits.float()

        loss = None
        if labels is not None:
            # Shift so that tokens < n predict n
            shift_logits = logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            shift_logits = shift_logits.view(-1, self.config.vocab_size)
            shift_labels = shift_labels.view(-1)
            # Ensure tensors are on the same device
            shift_labels = shift_labels.to(shift_logits.device)
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(shift_logits, shift_labels)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return (loss,) + output if loss is not None else output

        if labels is not None and self.model.training:
            loss += self.aux_loss_coef * outputs.aux_loss.to(loss.device)

        return JetMoECausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            aux_loss=outputs.aux_loss,
        )

    def prepare_inputs_for_generation(
        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
    ):
        # Omit tokens covered by past_key_values
        if past_key_values is not None:
            if isinstance(past_key_values, Cache):
                cache_length = past_key_values.get_seq_length()
                past_length = past_key_values.seen_tokens
                max_cache_length = past_key_values.get_max_length()
            else:
                cache_length = past_length = past_key_values[0][0].shape[2]
                max_cache_length = None

            # Keep only the unprocessed tokens:
            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
            # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
            # input)
            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
            # input_ids based on the past_length.
            elif past_length < input_ids.shape[1]:
                input_ids = input_ids[:, past_length:]
            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.

            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
            if (
                max_cache_length is not None
                and attention_mask is not None
                and cache_length + input_ids.shape[1] > max_cache_length
            ):
                attention_mask = attention_mask[:, -max_cache_length:]

        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            # create position_ids on the fly for batch generation
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values:
                position_ids = position_ids[:, -input_ids.shape[1] :]

        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
        if inputs_embeds is not None and past_key_values is None:
            model_inputs = {"inputs_embeds": inputs_embeds}
        else:
            model_inputs = {"input_ids": input_ids}

        model_inputs.update(
            {
                "position_ids": position_ids,
                "past_key_values": past_key_values,
                "use_cache": kwargs.get("use_cache"),
                "attention_mask": attention_mask,
            }
        )
        return model_inputs

    @staticmethod
    def _reorder_cache(past_key_values, beam_idx):
        reordered_past = ()
        for layer_past in past_key_values:
            reordered_past += (
                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
            )
        return reordered_past


@add_start_docstrings(
    """
    The JetMoE Model transformer with a sequence classification head on top (linear layer).

    [`JetMoEForSequenceClassification`] 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).
    """,
    MODULEFORMER_START_DOCSTRING,
)
# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->JetMoE, LLAMA->MODULEFORMER
class JetMoEForSequenceClassification(JetMoEPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = JetMoEModel(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(MODULEFORMER_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, ...,
            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:
                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
                sequence_lengths = sequence_lengths % input_ids.shape[-1]
                sequence_lengths = sequence_lengths.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 JetMoESequenceClassifierOutputWithPast(
            loss=loss,
            logits=pooled_logits,
            past_key_values=transformer_outputs.past_key_values,
            hidden_states=transformer_outputs.hidden_states,
            attentions=transformer_outputs.attentions,
            aux_loss=transformer_outputs.aux_loss,
        )