clean code

configuration_jetmoe.py

@@ -1,269 +0,0 @@
-""" JetMoE model configuration"""
-from collections import OrderedDict
-from typing import Any, List, Mapping, Optional
-
-from transformers import PreTrainedTokenizer, TensorType, is_torch_available
-from transformers.configuration_utils import PretrainedConfig
-from transformers.onnx import OnnxConfigWithPast, PatchingSpec
-from transformers.utils import logging
-import torch.nn.init as init
-import json
-
-logger = logging.get_logger(__name__)
-
-
-class JetMoEConfig(PretrainedConfig):
-    r"""
-    This is the configuration class to store the configuration of a [`JetMoEModel`]. It is used to instantiate a
-    JetMoE model according to the specified arguments, defining the model architecture. Instantiating a configuration
-    with the defaults will yield a similar configuration to that of the JetMoE
-    [jetmoe-small](https://huggingface.co/jetmoe-small) architecture. Configuration objects
-    inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from
-    [`PretrainedConfig`] for more information.
-
-    Args:
-        vocab_size (`int`, *optional*, defaults to 50400):
-            Vocabulary size of the JetMoE model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`JetMoEModel`].
-        n_positions (`int`, *optional*, defaults to 2048):
-            The maximum sequence length that this model might ever be used with. Typically set this to something large
-            just in case (e.g., 512 or 1024 or 2048).
-        n_embd (`int`, *optional*, defaults to 4096):
-            Dimensionality of the embeddings and hidden states.
-        n_layer (`int`, *optional*, defaults to 28):
-            Number of hidden layers in the Transformer encoder.
-        n_head (`int`, *optional*, defaults to 16):
-            Number of attention heads for each attention layer in the Transformer encoder.
-        rotary_dim (`int`, *optional*, defaults to 64):
-            Number of dimensions in the embedding that Rotary Position Embedding is applied to.
-        n_inner (`int`, *optional*, defaults to None):
-            Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
-        activation_function (`str`, *optional*, defaults to `"gelu_new"`):
-            Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
-        resid_pdrop (`float`, *optional*, defaults to 0.1):
-            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
-        embd_pdrop (`int`, *optional*, defaults to 0.1):
-            The dropout ratio for the embeddings.
-        attn_pdrop (`float`, *optional*, defaults to 0.1):
-            The dropout ratio for the attention.
-        layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
-            The epsilon to use in the layer normalization layers.
-        initializer_range (`float`, *optional*, defaults to 0.02):
-            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
-        use_cache (`bool`, *optional*, defaults to `True`):
-            Whether or not the model should return the last key/values attentions (not used by all models).
-
-    Example:
-
-    ```python
-    >>> from transformers import JetMoEConfig, JetMoEModel
-
-    >>> # Initializing a JetMoE 6B configuration
-    >>> configuration = JetMoEConfig()
-
-    >>> # Initializing a model (with random weights) from the configuration
-    >>> model = JetMoEModel(configuration)
-
-    >>> # Accessing the model configuration
-    >>> configuration = model.config
-    ```"""
-    model_type = "jetmoe"
-    attribute_map = {
-        "max_position_embeddings": "n_positions",
-        "hidden_size": "n_embd",
-        "num_attention_heads": "n_head",
-        "num_hidden_layers": "num_layers",
-    }
-
-    def __init__(
-        self,
-        vocab_size=50295,
-        hidden_size=1024,
-        num_layers=24,
-        num_attention_heads=16,
-        kv_channels = 128,
-        ffn_hidden_size=2048,
-        max_position_embeddings=4096,
-        rotary_percent=1.0,
-        activation_function="silu",
-        glu=True,
-        moe_num_experts=8,
-        moe_top_k=2,
-        use_cache=True,
-        bos_token_id=1,
-        eos_token_id=2,
-        tie_word_embeddings=True,
-        bias=True,
-        rope_theta=10000.0,
-        rms_norm_eps=1e-6,
-        initializer_range=0.01,
-        **kwargs,
-    ):
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_layers = num_layers
-        self.num_attention_heads = num_attention_heads
-        self.kv_channels = kv_channels
-        self.ffn_hidden_size = ffn_hidden_size
-        self.max_position_embeddings = max_position_embeddings
-        self.rotary_percent = rotary_percent
-        self.activation_function = activation_function
-        self.glu = glu
-        self.moe_num_experts = moe_num_experts
-        self.moe_top_k = moe_top_k
-        self.use_cache = use_cache
-        self.initializer_range = initializer_range
-
-        self.bos_token_id = bos_token_id
-        self.eos_token_id = eos_token_id
-
-        self.init_method = init.xavier_uniform_
-        self.output_layer_init_method = init.xavier_uniform_
-        self.bias = bias
-        self.rope_theta = rope_theta
-        self.rms_norm_eps = rms_norm_eps
-
-        super().__init__(
-            bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
-        )
-
-    def to_dict(self):
-        """Returns a dictionary representation of the config, excluding non-serializable attributes."""
-        return {k: v for k, v in self.__dict__.items() if k not in ['init_method', 'output_layer_init_method', 'torch_dtype', '_pre_quantization_dtype', 'quantization_config']}
-
-    def to_json_string(self, use_diff=False):
-        """Serializes this instance to a JSON string, excluding non-serializable attributes.
-        
-        Args:
-            use_diff (bool): Whether to use differences with the default config. This argument is
-                             accepted for compatibility with the transformers library but is not
-                             used in this custom implementation.
-        """
-        config_dict = self.to_dict()  # Assuming you have a to_dict method as shown earlier
-        return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
-
-class JetMoEOnnxConfig(OnnxConfigWithPast):
-    def __init__(
-        self,
-        config: PretrainedConfig,
-        task: str = "default",
-        patching_specs: List[PatchingSpec] = None,
-        use_past: bool = False,
-    ):
-        """
-        Initialize the JetMoEOnnxConfig.
-
-        Args:
-            config (PretrainedConfig): Pretrained model configuration.
-            task (str): Task description.
-            patching_specs (List[PatchingSpec]): List of patching specifications.
-            use_past (bool): Whether to use past tokens in the configuration.
-        """
-        super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
-        if not getattr(self._config, "pad_token_id", None):
-            # TODO: how to do that better?
-            self._config.pad_token_id = 0
-
-    @property
-    def inputs(self) -> Mapping[str, Mapping[int, str]]:
-        """
-        Define the input mappings.
-
-        Returns:
-            Mapping[str, Mapping[int, str]]: Input mappings.
-        """
-        common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}})
-        if self.use_past:
-            self.fill_with_past_key_values_(common_inputs, direction="inputs")
-            common_inputs["attention_mask"] = {0: "batch", 1: "past_sequence + sequence"}
-        else:
-            common_inputs["attention_mask"] = {0: "batch", 1: "sequence"}
-
-        return common_inputs
-
-    @property
-    def num_layers(self) -> int:
-        """
-        Get the number of layers.
-
-        Returns:
-            int: Number of layers.
-        """
-        return self._config.n_layer
-
-    @property
-    def num_attention_heads(self) -> int:
-        """
-        Get the number of attention heads.
-
-        Returns:
-            int: Number of attention heads.
-        """
-        return self._config.n_head
-
-    def generate_dummy_inputs(
-        self,
-        tokenizer: PreTrainedTokenizer,
-        batch_size: int = -1,
-        seq_length: int = -1,
-        is_pair: bool = False,
-        framework: Optional[TensorType] = None,
-    ) -> Mapping[str, Any]:
-        """
-        Generate dummy inputs for testing.
-
-        Args:
-            tokenizer (PreTrainedTokenizer): Pretrained tokenizer.
-            batch_size (int): Batch size.
-            seq_length (int): Sequence length.
-            is_pair (bool): Whether the input is a pair.
-            framework (Optional[TensorType]): Tensor framework.
-
-        Returns:
-            Mapping[str, Any]: Dummy inputs.
-        """
-        common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
-            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
-        )
-
-        # We need to order the input in the way they appears in the forward()
-        ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]})
-
-        # Need to add the past_keys
-        if self.use_past:
-            if not is_torch_available():
-                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
-            else:
-                import torch
-
-                batch, seqlen = common_inputs["input_ids"].shape
-                # Not using the same length for past_key_values
-                past_key_values_length = seqlen + 2
-                past_shape = (
-                    batch,
-                    self.num_attention_heads,
-                    past_key_values_length,
-                    self._config.hidden_size // self.num_attention_heads,
-                )
-                ordered_inputs["past_key_values"] = [
-                    (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers)
-                ]
-
-        ordered_inputs["attention_mask"] = common_inputs["attention_mask"]
-        if self.use_past:
-            mask_dtype = ordered_inputs["attention_mask"].dtype
-            ordered_inputs["attention_mask"] = torch.cat(
-                [ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)], dim=1
-            )
-
-        return ordered_inputs
-
-    @property
-    def default_onnx_opset(self) -> int:
-        """
-        Get the default ONNX opset version.
-
-        Returns:
-            int: Default ONNX opset version.
-        """
-        return 13

modeling_jetmoe.py

@@ -1,1399 +0,0 @@
-""" 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
-
-import megablocks
-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
-from jetmoe_model.utils import moe
-
-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
-
-logger = logging.get_logger(__name__)
-
-_CHECKPOINT_FOR_DOC = "jetmoe"
-_CONFIG_FOR_DOC = "JetMoEConfig"
-
-
-@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.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.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,
-        )