Add Arctic modeling/config/tokenization files (#3)

- Add Arctic modeling/config/tokenization files (2af13d229ef34519e40d05fcadb390e0e25c0a2f)
- Update AutoTokenizer path (ecca7ac97dd6c7365a5c65f61868101d7c624659)
- Revert extraneous tokenizer config changes (f9281b708c4e07b2348d2f7d71a9816d32eca8a4)


Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>

config.json

@@ -3,6 +3,12 @@
     "ArcticForCausalLM"
   ],
   "attention_dropout": 0,
+  "auto_map": {
+    "AutoConfig": "configuration_arctic.ArcticConfig",
+    "AutoModel": "modeling_arctic.ArcticModel",
+    "AutoModelForCausalLM": "modeling_arctic.ArcticForCausalLM",
+    "AutoModelForSequenceClassification": "modeling_arctic.ArcticForSequenceClassification"
+  },
   "bos_token_id": 31998,
   "enable_expert_tensor_parallelism": false,
   "enc_index": [

configuration_arctic.py

@@ -0,0 +1,216 @@
+# Copyright 2023 Snowflake AI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Arctic model configuration"""
+
+from dataclasses import asdict, dataclass
+from typing import Any, Dict
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+ARCTIC_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "arctic": "https://huggingface.co/Snowflake/snowflake-arctic-instruct/tree/main/config.json",
+}
+
+
+@dataclass
+class ArcticLoraConfig:
+    lora_r: int = 64
+    lora_alpha: float = 16
+    shard_base_weights: bool = False
+
+
+@dataclass
+class ArcticQuantizationConfig:
+    q_bits: int = 8
+    rounding: str = "nearest"
+    mantissa_bits: int = 3
+    group_size: int = 512
+
+
+class ArcticConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`ArcticModel`]. It is used to instantiate an
+    Arctic 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 #TODO(rsamdani): add what model has the default config..
+
+
+    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 32000):
+            Vocabulary size of the Arctic model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`ArcticModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 14336):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
+            The maximum sequence length that this model might ever be used with. Arctic's sliding window attention
+            allows sequence of up to 4096*32 tokens.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            The id of the padding token.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            The id of the "beginning-of-sequence" token.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            The id of the "end-of-sequence" token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        rope_theta (`float`, *optional*, defaults to 1000000.0):
+            The base period of the RoPE embeddings.
+        sliding_window (`int`, *optional*):
+            Sliding window attention window size. If not specified, will default to `4096`.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_experts_per_tok (`int`, *optional*, defaults to 2):
+            The number of experts to root per-token, can be also interpreted as the `top-p` routing
+            parameter
+        num_local_experts (`int`, *optional*, defaults to 8):
+            Number of experts per Sparse MLP layer.
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.001):
+            The aux loss factor for the total loss.
+
+    ```python
+    >>> from transformers import ArcticModel, ArcticConfig
+
+    >>> # Initializing a Arctic 7B style configuration TODO(rsamdani): verify which model does the default configuration correspond to.
+    >>> configuration = ArcticConfig()
+
+    >>> # Initializing a model from the Arctic 7B style configuration
+    >>> model = ArcticModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "arctic"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=1e6,
+        sliding_window=None,
+        attention_dropout=0.0,
+        num_experts_per_tok=1,
+        num_local_experts=8,
+        router_aux_loss_coef=0.001,
+        moe_layer_frequency=2,
+        parallel_attn_mlp_res=False,
+        moe_train_capacity_factor=1,
+        moe_eval_capacity_factor=1,
+        enable_expert_tensor_parallelism=False,
+        moe_min_capacity=0,
+        moe_token_dropping=True,
+        quantization=None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.attention_dropout = attention_dropout
+
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_local_experts = num_local_experts
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.moe_layer_frequency = moe_layer_frequency
+        self.moe_train_capacity_factor = moe_train_capacity_factor
+        self.moe_eval_capacity_factor = moe_eval_capacity_factor
+        self.enable_expert_tensor_parallelism = enable_expert_tensor_parallelism
+        self.moe_min_capacity = moe_min_capacity
+        self.moe_token_dropping = moe_token_dropping
+        self.parallel_attn_mlp_res = parallel_attn_mlp_res
+        if isinstance(quantization, dict):
+            self.quantization = ArcticQuantizationConfig(**quantization)
+        else:
+            self.quantization = quantization
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    @classmethod
+    def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "ArcticConfig":
+        result = super().from_dict(config_dict, **kwargs)
+        if isinstance(result, tuple):
+            config = result[0]
+        else:
+            config = result
+        if isinstance(config.quantization, dict):
+            config.quantization = ArcticQuantizationConfig(**config.quantization)
+        return result
+
+    def to_dict(self) -> Dict[str, Any]:
+        ret = super().to_dict()
+        if isinstance(ret["quantization"], ArcticQuantizationConfig):
+            ret["quantization"] = asdict(ret["quantization"])
+        return ret

modeling_arctic.py

@@ -0,0 +1,1943 @@
+# coding=utf-8
+# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Arctic model."""
+import copy
+import inspect
+import time
+import math
+import warnings
+import re
+from typing import List, Optional, Tuple, Union
+
+import deepspeed
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import (
+    _prepare_4d_causal_attention_mask,
+    _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from transformers.modeling_outputs import (
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_13
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+from .configuration_arctic import ArcticConfig
+from transformers.integrations.deepspeed import is_deepspeed_available 
+from transformers.utils.versions import require_version
+
+if is_deepspeed_available():
+    from deepspeed.moe.layer import MoE 
+    # Note that below will crash if there is an available deepspeed that does not have ds_linear.
+    try:
+        import deepspeed.linear as ds_linear
+    except Exception:
+        pass
+else:
+    MoE = None
+
+if is_flash_attn_2_available():
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+
+    _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+    if not is_torch_greater_or_equal_than_1_13:
+        import torch.fx
+
+    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "ArcticConfig"
+USE_DEEPSPEED_MOE_ARG = "use_deepspeed_moe_implementation"
+MOE_EXPERT_PARALLEL_SIZE_ARG = "moe_expert_parallel_size"
+DEEPSPEED_QUANTIZATION_CONFIG = "deepspeed_quantization"
+DEEPSPEED_LORA_CONFIG = "deepspeed_lora"
+QUANTIZATION_CONFIG = "ds_quantization_config"
+
+# REQUIRED_DEEPSPEED_VERSION = "deepspeed>0.14.5"
+# def is_deepspeed_valid_and_available(raise_error=False, error_msg=""):
+#     available_and_valid = True
+#     if not is_deepspeed_available():
+#         available_and_valid = False
+#         if raise_error:
+#             raise ValueError(f"DeepSpeed is required for this feature, {error_msg}")
+#     else:
+        
+#     return available_and_valid
+
+def load_balancing_loss_func(
+    gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=4, attention_mask: Optional[torch.Tensor] = None
+) -> float:
+    r"""
+    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+    See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
+    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+    experts is too unbalanced.
+
+    Args:
+        gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
+            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+            shape [batch_size X sequence_length, num_experts].
+        attention_mask (`torch.Tensor`, None):
+            The attention_mask used in forward function
+            shape [batch_size X sequence_length] if not None.
+        num_experts (`int`, *optional*):
+            Number of experts
+
+    Returns:
+        The auxiliary loss.
+    """
+    if gate_logits is None or not isinstance(gate_logits, tuple):
+        return 0
+
+    if isinstance(gate_logits, tuple):
+        compute_device = gate_logits[0].device
+        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+    if attention_mask is None:
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.mean(routing_weights, dim=0)
+    else:
+        batch_size, sequence_length = attention_mask.shape
+        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+        expert_attention_mask = (
+            attention_mask[None, :, :, None, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, 2, num_experts))
+            .reshape(-1, 2, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+            expert_attention_mask, dim=0
+        )
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+        router_per_expert_attention_mask = (
+            attention_mask[None, :, :, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
+            .reshape(-1, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+            router_per_expert_attention_mask, dim=0
+        )
+
+    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
+    return overall_loss * num_experts
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Arctic
+class ArcticRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        ArcticRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Arctic
+class ArcticRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # 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=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->Arctic
+class ArcticAttention(nn.Module):
+    """
+    Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+    and "Generating Long Sequences with Sparse Transformers".
+    """
+
+    def __init__(self, config: ArcticConfig, layer_idx: Optional[int] = None,  **kwargs):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+        self.use_deepspeed_implementation = USE_DEEPSPEED_MOE_ARG in kwargs and kwargs[USE_DEEPSPEED_MOE_ARG]
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        deepspeed_quantization = kwargs.get(DEEPSPEED_QUANTIZATION_CONFIG)
+        deepspeed_lora_config = kwargs.get(DEEPSPEED_LORA_CONFIG)
+        quantization_config = kwargs.get(QUANTIZATION_CONFIG, None)
+
+        self.q_proj = get_arctic_linear(self.hidden_size, self.num_heads * self.head_dim, bias=False,
+                                     use_deepspeed_implementation=self.use_deepspeed_implementation,
+                                     ds_optimized_lora_config=deepspeed_lora_config, 
+                                     ds_optimized_quantization_config=quantization_config, 
+                                     ds_optimized_base_weight_sharding=True,
+                                     dtype=torch.bfloat16)
+        self.k_proj = get_arctic_linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False,
+                                     use_deepspeed_implementation=self.use_deepspeed_implementation,                                     
+                                     ds_optimized_lora_config=deepspeed_lora_config, 
+                                     ds_optimized_quantization_config=quantization_config, 
+                                     ds_optimized_base_weight_sharding=True,
+                                     dtype=torch.bfloat16)
+        self.v_proj = get_arctic_linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False,
+                                     use_deepspeed_implementation=self.use_deepspeed_implementation,
+                                     ds_optimized_lora_config=deepspeed_lora_config, 
+                                     ds_optimized_quantization_config=quantization_config, 
+                                     ds_optimized_base_weight_sharding=True,
+                                     dtype=torch.bfloat16)
+        self.o_proj = get_arctic_linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False,
+                                     use_deepspeed_implementation=self.use_deepspeed_implementation,
+                                     ds_optimized_lora_config=deepspeed_lora_config, 
+                                     ds_optimized_quantization_config=quantization_config, 
+                                     ds_optimized_base_weight_sharding=True,
+                                     dtype=torch.bfloat16)
+        
+        self.rotary_emb = ArcticRotaryEmbedding(
+            self.head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            base=self.rope_theta,
+        )
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            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.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+# Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->Arctic
+class ArcticFlashAttention2(ArcticAttention):
+    """
+    Arctic flash attention module. This module inherits from `ArcticAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ):
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+        # Because the input can be padded, the absolute sequence length depends on the max position id.
+        rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
+        cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        use_sliding_windows = (
+            _flash_supports_window_size
+            and getattr(self.config, "sliding_window", None) is not None
+            and kv_seq_len > self.config.sliding_window
+        )
+
+        if not _flash_supports_window_size:
+            logger.warning_once(
+                "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
+                " make sure to upgrade flash-attn library."
+            )
+
+        if past_key_value is not None:
+            # Activate slicing cache only if the config has a value `sliding_windows` attribute
+            cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
+            if (
+                getattr(self.config, "sliding_window", None) is not None
+                and kv_seq_len > self.config.sliding_window
+                and cache_has_contents
+            ):
+                slicing_tokens = 1 - self.config.sliding_window
+
+                past_key = past_key_value[self.layer_idx][0]
+                past_value = past_key_value[self.layer_idx][1]
+
+                past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+                past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+                if past_key.shape[-2] != self.config.sliding_window - 1:
+                    raise ValueError(
+                        f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+                        f" {past_key.shape}"
+                    )
+
+                if attention_mask is not None:
+                    attention_mask = attention_mask[:, slicing_tokens:]
+                    attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
+
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # repeat k/v heads if n_kv_heads < n_heads
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+        dropout_rate = 0.0 if not self.training else self.attention_dropout
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in float16 just to be sure everything works as expected.
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        # Reashape to the expected shape for Flash Attention
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        attn_output = self._flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            dropout=dropout_rate,
+            use_sliding_windows=use_sliding_windows,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self,
+        query_states,
+        key_states,
+        value_states,
+        attention_mask,
+        query_length,
+        dropout=0.0,
+        softmax_scale=None,
+        use_sliding_windows=False,
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+            use_sliding_windows (`bool`, *optional*):
+                Whether to activate sliding window attention.
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+            if not use_sliding_windows:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output_unpad = flash_attn_varlen_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cu_seqlens_q=cu_seqlens_q,
+                    cu_seqlens_k=cu_seqlens_k,
+                    max_seqlen_q=max_seqlen_in_batch_q,
+                    max_seqlen_k=max_seqlen_in_batch_k,
+                    dropout_p=dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            if not use_sliding_windows:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                )
+            else:
+                attn_output = flash_attn_func(
+                    query_states,
+                    key_states,
+                    value_states,
+                    dropout,
+                    softmax_scale=softmax_scale,
+                    causal=causal,
+                    window_size=(self.config.sliding_window, self.config.sliding_window),
+                )
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+
+        # On the first iteration we need to properly re-create the padding mask
+        # by slicing it on the proper place
+        if kv_seq_len != attention_mask.shape[-1]:
+            attention_mask_num_tokens = attention_mask.shape[-1]
+            attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+
+        key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+        value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+def get_arctic_linear(input_dim, 
+                   output_dim, 
+                   bias=False,
+                   use_deepspeed_implementation=False,
+                   ds_optimized_lora_config=None, 
+                   ds_optimized_quantization_config=None, 
+                   ds_optimized_base_weight_sharding=False,
+                   dtype=torch.bfloat16):
+    """Can return deepspeed optimized linear if available.
+    Args:
+        input_dim, output_dim, bias, dtype: self explanatory (same as from nn.Linear)
+        ds_optimized_lora_config: config of type ds_linear.LoRAConfig that contains lora specific parameter if we want to add lora to this layer.
+        ds_optimized_quantization_config: config of type ds_linear.QuantizationConfig.
+        ds_optimized_base_weight_sharding: bool. If true, the base weight for lora (provided ds_optimized_lora_config is not None) will be sharded across all available gpus
+        in a tensor parallel way.
+    """
+    if is_deepspeed_available():
+        if ds_optimized_lora_config is not None:
+            ds_optimized_lora_config: ds_linear.LoRAConfig = copy.deepcopy(ds_optimized_lora_config)
+            ds_optimized_lora_config.base_weight_sharding = torch.distributed.get_world_size() if ds_optimized_base_weight_sharding else 1
+        return ds_linear.OptimizedLinear(input_dim, output_dim, bias, ds_optimized_lora_config, ds_optimized_quantization_config, dtype=dtype)
+    return nn.Linear(input_dim, output_dim, bias=bias, dtype=dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaSdpaAttention with Llama->Arctic
+class ArcticSdpaAttention(ArcticAttention):
+    """
+    Arctic attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `ArcticAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from ArcticAttention.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(
+                "ArcticModel is using ArcticSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        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=self.attention_dropout if self.training else 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.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+MIXTRAL_ATTENTION_CLASSES = {
+    "eager": ArcticAttention,
+    "flash_attention_2": ArcticFlashAttention2,
+    "sdpa": ArcticSdpaAttention,
+}
+
+
+class ArcticMLP(nn.Module):
+    def __init__(self, config: ArcticConfig, 
+                 use_deepspeed_implementation=False,
+                 ds_optimized_lora_config=None,
+                 ds_optimized_quantization_config=None,
+                 shard_base_weights_if_doing_lora=False, 
+                 is_residual_mlp=False):
+        """MLP class for Arctic supporting vanilla linear layers as well as some deepspeed optimizations.
+
+        ds_optimized_lora_config: config of type ds_linear.LoRAConfig that contains lora specific parameter if we want to add lora to this layer.
+        ds_optimized_quantization_config: config of type ds_linear.QuantizationConfig.
+        ds_optimized_base_weight_sharding: bool. If true, the base weight for lora (provided ds_optimized_lora_config is not None) will be sharded across all available gpus
+        in a tensor parallel way.
+        is_residual_mlp: bool. If true, this is MLP inside arctic residual layer which has ffn_dim the same as full intermediate_size.
+        """        
+        super(ArcticMLP, self).__init__()
+        self.hidden_dim = config.hidden_size
+        self.ffn_dim = config.intermediate_size if not is_residual_mlp else self.hidden_dim  
+        self.w1 = get_arctic_linear(self.hidden_dim, self.ffn_dim, False,
+                                 use_deepspeed_implementation=use_deepspeed_implementation,
+                                 ds_optimized_lora_config=ds_optimized_lora_config, 
+                                 ds_optimized_quantization_config=ds_optimized_quantization_config, 
+                                 ds_optimized_base_weight_sharding=shard_base_weights_if_doing_lora,
+                                 dtype=torch.bfloat16)
+        self.w2 = get_arctic_linear(self.ffn_dim, self.hidden_dim, False,
+                                 use_deepspeed_implementation=use_deepspeed_implementation,                                 
+                                 ds_optimized_lora_config=ds_optimized_lora_config, 
+                                 ds_optimized_quantization_config=ds_optimized_quantization_config, 
+                                 ds_optimized_base_weight_sharding=shard_base_weights_if_doing_lora,
+                                 dtype=torch.bfloat16)
+        self.w3 = get_arctic_linear(self.hidden_dim, self.ffn_dim, False,
+                                 use_deepspeed_implementation=use_deepspeed_implementation,                                 
+                                 ds_optimized_lora_config=ds_optimized_lora_config, 
+                                 ds_optimized_quantization_config=ds_optimized_quantization_config, 
+                                 ds_optimized_base_weight_sharding=shard_base_weights_if_doing_lora,
+                                 dtype=torch.bfloat16)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states):
+        current_hidden_states = self.act_fn(self.w1(hidden_states)) * self.w3(hidden_states)
+        current_hidden_states = self.w2(current_hidden_states)
+        return current_hidden_states
+
+
+class ArcticMoE(nn.Module):
+    def __init__(self, config: ArcticConfig, layer_id: int, **kwargs):
+        super(ArcticMoE, self).__init__()
+
+        self.hidden_dim = config.hidden_size
+        self.num_experts = config.num_local_experts
+        self.layer_id = layer_id  
+        self.top_k = config.num_experts_per_tok
+        self.is_moe_layer = (layer_id+1) % config.moe_layer_frequency == 0
+
+        self.use_deepspeed_implementation = USE_DEEPSPEED_MOE_ARG in kwargs and kwargs[USE_DEEPSPEED_MOE_ARG]
+        if self.use_deepspeed_implementation and MoE is None:
+            raise ValueError("Deepspeed is not installed")
+        quantization_config = kwargs.get(QUANTIZATION_CONFIG, None)
+        deepspeed_lora = kwargs.get(DEEPSPEED_LORA_CONFIG)
+        if not self.is_moe_layer: # dense, not MoE
+            self.mlp = ArcticMLP(config,
+                              use_deepspeed_implementation=self.use_deepspeed_implementation,
+                              ds_optimized_quantization_config=quantization_config,
+                              ds_optimized_lora_config=deepspeed_lora,
+                              shard_base_weights_if_doing_lora=True)
+        else:
+            if self.use_deepspeed_implementation: # DeepSpeed's MoE         
+                moe_expert_parallel_size = kwargs.get(MOE_EXPERT_PARALLEL_SIZE_ARG, 1)
+                self.mlp = MoE(self.hidden_dim,
+                                # base weight sharding false for all deepspeed moe calls because it is already sharded
+                                ArcticMLP(config, 
+                                       use_deepspeed_implementation=True,
+                                       ds_optimized_quantization_config=quantization_config,
+                                       ds_optimized_lora_config=deepspeed_lora,
+                                       shard_base_weights_if_doing_lora=False),
+                                num_experts=config.num_local_experts,
+                                ep_size=moe_expert_parallel_size,
+                                k=config.num_experts_per_tok,
+                                use_residual=False,
+                                capacity_factor=config.moe_train_capacity_factor,
+                                eval_capacity_factor=config.moe_eval_capacity_factor,
+                                enable_expert_tensor_parallelism=config.enable_expert_tensor_parallelism,
+                                min_capacity=config.moe_min_capacity,
+                                drop_tokens=config.moe_token_dropping
+                                )
+            else:
+                # "local" MoE implementation
+                self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+                self.experts = nn.ModuleList([ArcticMLP(config,
+                                                     use_deepspeed_implementation=self.use_deepspeed_implementation,                                       
+                                                     ds_optimized_quantization_config=quantization_config,
+                                                     ds_optimized_lora_config=deepspeed_lora,
+                                                     shard_base_weights_if_doing_lora=True) for i in range(self.num_experts)])
+
+        # if torch.distributed.get_rank() == 0:
+        #     deepspeed.runtime.utils.see_memory_usage("", force=True)
+
+
+    # Similar in behavior to transformers.models.mixtral.modeling_mixtral.MixtralSparseMoeBlock.forward but more efficient.
+    def _moe_foreward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits = self.gate(hidden_states)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+        if self.top_k > 1:
+            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        # we cast back to the input dtype
+
+        final_hidden_states = torch.zeros(
+            (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+        )
+
+        # Matching between experts, tokens, and their top-k rank. For every i,
+        # expert_idx[i] is the rank topk_idx[i] expert for token_idx[i].
+        expert_idx, token_idx, topk_idx = torch.where(
+            selected_experts == torch.arange(
+                self.num_experts,
+                device=selected_experts.device,
+            ).view((self.num_experts, 1, 1))
+        )
+
+        # Split into one chunk per expert.
+        bincount = torch.bincount(expert_idx, minlength=self.num_experts).tolist()
+        token_idx = token_idx.split(bincount)
+        topk_idx = topk_idx.split(bincount)
+
+        # Loop over all available experts in the model and perform the computation on each expert
+        for expert_layer, top_x, idx in zip(self.experts, token_idx, topk_idx):
+            if top_x.shape[0] == 0:
+                continue
+
+            # in torch it is faster to index using lists than torch tensors
+            top_x_list = top_x.tolist()
+            idx_list = idx.tolist()
+
+            # Index the correct hidden states and compute the expert hidden state for
+            # the current expert. We need to make sure to multiply the output hidden
+            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+            current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
+            current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
+
+            # However `index_add_` only support torch tensors for indexing so we'll use
+            # the `top_x` tensor here.
+            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+            # torch.distributed.barrier()
+        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return final_hidden_states, load_balancing_loss_func((router_logits, ), self.num_experts, self.top_k) # ZY: let's directly output the loss to align what we have in ds
+
+    def forward(self, hidden_states: torch.Tensor):
+        if self.is_moe_layer:
+            if self.use_deepspeed_implementation:
+                # deepspeed returns a tuple including output, gate loss, and expert count.
+                hidden_states, moe_loss, _ = self.mlp(hidden_states)
+                return hidden_states, moe_loss
+            else:
+                return self._moe_foreward(hidden_states)
+        else:
+            return self.mlp(hidden_states), torch.tensor(0.0, device=hidden_states.device, dtype=hidden_states.dtype)
+
+
+class ArcticDecoderLayer(nn.Module):
+    def __init__(self, config: ArcticConfig, layer_idx: int, **kwargs):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.hidden_size = config.hidden_size
+        self.self_attn = MIXTRAL_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx, **kwargs)
+        self.block_sparse_moe = ArcticMoE(config, layer_id=layer_idx, **kwargs)
+        self.input_layernorm = ArcticRMSNorm(config.hidden_size, eps=config.rms_norm_eps) 
+        self.post_attention_layernorm = ArcticRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.use_deepspeed_implementation = USE_DEEPSPEED_MOE_ARG in kwargs and kwargs[USE_DEEPSPEED_MOE_ARG]
+
+        self.parallel_attn_mlp_res = config.parallel_attn_mlp_res and self.block_sparse_moe.is_moe_layer # add residual only when it is moe layer
+        deepspeed_quantization = kwargs.get(DEEPSPEED_QUANTIZATION_CONFIG)
+        deepspeed_lora = kwargs.get(DEEPSPEED_LORA_CONFIG)
+        if self.parallel_attn_mlp_res:
+            self.residual_layernorm = ArcticRMSNorm(config.hidden_size, eps=config.rms_norm_eps) 
+            self.residual_mlp =  ArcticMLP(config,
+                                        use_deepspeed_implementation=self.use_deepspeed_implementation,
+                                        is_residual_mlp=True,
+                                        ds_optimized_quantization_config=deepspeed_quantization,
+                                        ds_optimized_lora_config=deepspeed_lora,
+                                        shard_base_weights_if_doing_lora=True) # for the residual layer. always shard the base weight if doing deepspeed lora.
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        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.`"
+            )
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, sequence_length)` where padding elements are indicated by 0.
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+        """
+
+        residual_input = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual_input + hidden_states
+
+        residual_attn = hidden_states
+        
+        if self.parallel_attn_mlp_res:
+            # Note the architecture here is that the MOE layers reads the **pre-attention** input while there is a "normal" transformer residual part.
+            # This is to achieve better parallelization.
+
+            # residual mlp part
+
+            hidden_states = self.residual_layernorm(hidden_states)
+            hidden_states = self.residual_mlp(hidden_states)
+            residual_residual = residual_attn + hidden_states
+            # parallel mlp moe part
+            hidden_states = self.post_attention_layernorm(residual_input) # parallel attn mlp has the same input
+            hidden_states, gate_loss = self.block_sparse_moe(hidden_states)
+            hidden_states = residual_residual + hidden_states
+        else:
+            hidden_states = self.post_attention_layernorm(hidden_states)
+            hidden_states, gate_loss = self.block_sparse_moe(hidden_states)
+            hidden_states = residual_attn + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        outputs += (gate_loss,)
+
+        return outputs
+
+
+ARCTIC_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`ArcticConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare Arctic Model outputting raw hidden-states without any specific head on top.",
+    ARCTIC_START_DOCSTRING,
+)
+# Copied from transformers.models.mistral.modeling_mistral.MistralPreTrainedModel with Mistral->Arctic
+class ArcticPreTrainedModel(PreTrainedModel):
+    config_class = ArcticConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["ArcticDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        # if is_deepspeed_available():
+        #     # TODO(rajhans): remove this once ds has init for quantizedlinear.
+        #     try:
+        #         from deepspeed.linear.quantization import QuantizedLinear, QuantizedParameter
+        #     if isinstance(module, QuantizedLinear):
+        #         weights = module.weight.dequantized()
+        #         weights.normal_(mean=0.0, std=std)
+        #         if module.bias is not None:
+        #             module.bias.data.zero_()
+        #         module.weight = QuantizedParameter(weights)
+        #         module.weight.to(dtype=torch.bfloat16, device=weights.device)
+        # el
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+MIXTRAL_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`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 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 in the cross-attention
+            blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare Arctic Model outputting raw hidden-states without any specific head on top.",
+    ARCTIC_START_DOCSTRING,
+)
+# Copied from transformers.models.mistral.modeling_mistral.MistralModel with MISTRAL->MIXTRAL,Mistral->Arctic
+class ArcticModel(ArcticPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`ArcticDecoderLayer`]
+
+    Args:
+        config: ArcticConfig
+    """
+
+    def __init__(self, config: ArcticConfig, **kwargs):
+        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(
+            [ArcticDecoderLayer(config, layer_idx, **kwargs) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._attn_implementation = config._attn_implementation
+        self.norm = ArcticRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = True
+        # 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
+
+    # Ignore copy
+    @add_start_docstrings_to_model_forward(MIXTRAL_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, MoeModelOutputWithPast]:
+        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")
+
+        past_key_values_length = 0
+
+        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
+
+        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 Arctic. 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,
+                sliding_window=self.config.sliding_window,
+            )
+
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        all_router_losses = ()
+        next_decoder_cache = None
+
+        for i, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                if hasattr(layer_outputs[2 if output_attentions else 1], 'to_legacy_cache'):
+                    next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+                else:
+                    if next_decoder_cache is None:
+                        next_decoder_cache = [layer_outputs[2 if output_attentions else 1]]
+                    else:
+                        next_decoder_cache.append(layer_outputs[2 if output_attentions else 1])
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+            all_router_losses += (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 and hasattr(next_decoder_cache, 'to_legacy_cache') else next_decoder_cache
+        torch.cuda.empty_cache()        
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_losses]
+                if v is not None
+            )
+        return MoeModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            router_logits=all_router_losses,
+        )
+
+class ArcticForCausalLM(ArcticPreTrainedModel):
+    # TODO(jeffra): update _keys_to_ignore_on_load_unexpected with expert keys not relevant for this rank
+    _keys_to_ignore_on_load_unexpected = [r"model\.layers\.\d+\.block_sparse_moe\.experts\.\d+\.w\d+\.weight"
+                                          r"model\.layers\.\d+\.block_sparse_moe\.gate\.weight"]
+    _keys_to_ignore_on_load_missing = [r"model\.layers\.\d+\.block_sparse_moe\.mlp\.deepspeed_moe\.experts\.deepspeed_experts\.\d+\.w\d+\.weight",
+                                       r"model\.layers\.\d+\.block_sparse_moe\.mlp\.deepspeed_moe\.gate\.wg\.weight"]
+    _tied_weights_keys = []#["lm_head.weight"]
+
+    def __init__(self, config, **kwargs):
+        super().__init__(config)
+        self.model = ArcticModel(config, **kwargs)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.router_aux_loss_coef = config.router_aux_loss_coef
+        self.num_experts = config.num_local_experts
+        self.num_experts_per_tok = config.num_experts_per_tok
+        self.use_deepspeed_moe = kwargs.get(USE_DEEPSPEED_MOE_ARG, False)
+        self.moe_expert_parallel_size = kwargs.get(MOE_EXPERT_PARALLEL_SIZE_ARG, 1)
+        self.is_deepspeed_lora = kwargs.get(DEEPSPEED_LORA_CONFIG) is not None
+        self.gradient_checkpointing = True
+        # self.shard_base_weights_if_doing_lora = kwargs.get("shard_base_weights_if_doing_lora", False)
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+
+    def _expert_number_from_param_name(self, param_name):
+        # example param_name: model.layers.1.block_sparse_moe.experts.10.w1.weight
+        pattern = r'experts\.(\d+)\.'
+        m = re.search(pattern, param_name)
+        if m:
+            return int(m[1])
+        else:
+            return None
+
+    def state_dict(self, *args, **kwargs):
+        state_dict = super().state_dict(*args, **kwargs)
+
+        if not self.use_deepspeed_moe:
+            return state_dict
+
+        # when trying to construct the deepspeed checkpoint we don't want to gather everything
+        if not getattr(self, '_gather_expert_params', False):
+            return state_dict
+
+        rank = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0
+        world_size = torch.distributed.get_world_size() if torch.distributed.is_initialized() else 1
+
+        # non-lora experts
+        pattern = r"model\.layers\.\d+\.block_sparse_moe\.mlp\.deepspeed_moe\.experts\.deepspeed_experts\.\d+\.w\d+\.weight"
+        expert_params = [s for s in state_dict.keys() if re.search(pattern, s)]
+
+        for param_name in expert_params:
+            param_tensor = state_dict[param_name].to('cuda')
+            output = [torch.zeros_like(param_tensor) for _ in range(world_size)]
+            torch.distributed.gather(param_tensor, gather_list=output if rank == 0 else None, dst=0, group=None)
+            # rename from local rank to global rank
+            for gather_rank, gather_param in enumerate(output):
+                experts_per_rank = self.num_experts // self.moe_expert_parallel_size
+                new_expert_number = gather_rank * experts_per_rank + self._expert_number_from_param_name(param_name)
+                new_param_name = re.sub(r'(experts\.)(\d+)(\.)', rf'\g<1>{new_expert_number}\3', param_name)
+                state_dict[new_param_name] = gather_param
+                if rank == 0:
+                    print(f"adding to state_dict and renaming: {param_name} -> {new_param_name}")
+    
+        # Handle custom LoRA implementation  
+        # TODO(rajhans): the part below is untested and shows up when doing lora training. Should not affect inference.
+        if self.is_deepspeed_lora:
+            for param_name in list(state_dict.keys()):  # Use list to avoid RuntimeError due to changing size during iteration  
+                if param_name.endswith("base_weight"):  
+                    base_weight = state_dict[param_name].to('cuda')
+                    
+                    # If the base weight is sharded, gather weights from multiple ranks and concatenate 
+                    # except if the weights are from deespeed_moe which is not sharded (due to EP). 
+                    if self.shard_base_weights_if_doing_lora and 'deepspeed_moe.experts.deepspeed_experts' not in param_name:
+                        gathered_weights = [torch.zeros_like(base_weight, 
+                                                             device=base_weight.device, dtype=base_weight.dtype) for _ in range(world_size)]
+                        torch.distributed.gather(base_weight, gather_list=gathered_weights if rank == 0 else None, dst=0, group=None)  
+                        base_weight = torch.cat(gathered_weights, dim=1)
+
+
+                    ## The part below is useful if we want to output HF transformer path weights, but commenting it for now 
+                    # Merge the LoRA weights into the base weights  
+                    # lora_weight_1 = state_dict.get(param_name.replace("base_weight", "lora_weight_1.weight"))  
+                    # lora_weight_2 = state_dict.get(param_name.replace("base_weight", "lora_weight_2.weight"))  
+                    # if lora_weight_1 is not None and lora_weight_2 is not None:
+                    #     lora_weights = torch.matmul(lora_weight_2, lora_weight_1)
+                    #     base_weight += lora_weights
+                    # else:
+                    #     raise ValueError                  
+
+                    # # Rename the base weight to weight  
+                    # new_param_name = param_name.replace("base_weight", "weight")  
+                    # state_dict[new_param_name] = base_weight  
+                    
+                    # Remove the base weight from the state dict  
+                    # del state_dict[param_name]    
+        return state_dict      
+
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+        if not self.use_deepspeed_moe:
+            return super()._load_from_state_dict(
+                state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+            )
+
+        world_size = torch.distributed.get_world_size() if torch.distributed.is_initialized() else 1
+        #TODO(jeffra): currently assumes fine-tuning only on one node, fix for world_size != ep size
+        if self.moe_expert_parallel_size > 1:
+            assert self.moe_expert_parallel_size == world_size, \
+                    f"currently only support expert parallel size equal to world size but {self.moe_expert_parallel_size=} and {world_size=}"
+
+        rank = torch.distributed.get_rank() if torch.distributed.is_initialized() else 0
+        num_local_experts = self.num_experts // self.moe_expert_parallel_size
+        local_expert_range = range(num_local_experts * rank, num_local_experts * rank + num_local_experts)
+
+        # no deepspeed
+        #   model.layers.1.block_sparse_moe.experts.10.w1.weight
+        #   model.layers.1.block_sparse_moe.gate.weight
+        # w. deepspeed
+        #   model.layers.1.block_sparse_moe.mlp.deepspeed_moe.gate.wg.weight
+        #   model.layers.1.block_sparse_moe.mlp.deepspeed_moe.experts.deepspeed_experts.10.w1.weight
+
+        gate_pattern = r'model\.layers\.\d+\.block_sparse_moe\.gate\.weight'
+
+        expert_params_to_keep = []
+        expert_params_to_remove = []
+        gate_params = []
+        for param_name in state_dict.keys():
+            expert_number = self._expert_number_from_param_name(param_name)
+            if expert_number is not None:
+                if expert_number in local_expert_range:
+                    expert_params_to_keep.append(param_name)
+                else:
+                    expert_params_to_remove.append(param_name)
+            elif re.search(gate_pattern, param_name):
+                gate_params.append(param_name)
+
+        # drop all experts in the state_dict that we don't need locally
+        for param_name in expert_params_to_remove:
+            print(f'{rank=} dropping {param_name}')
+            del state_dict[param_name]
+
+        # rename remaining experts to align with the local config
+        for param_name in expert_params_to_keep:
+            # adjust expert number wrt expert parallelism
+            new_expert_number = self._expert_number_from_param_name(param_name) % num_local_experts
+            new_param_name = re.sub(r'(experts\.)(\d+)(\.)', rf'\g<1>{new_expert_number}\3', param_name)
+
+            # use deepspeed moe param path
+            split_param_name = new_param_name.split('.')
+            idx = split_param_name.index('experts')
+            ds_moe_path = "mlp.deepspeed_moe.experts.deepspeed_experts".split('.')
+            new_param_name = split_param_name[0:idx] + ds_moe_path + split_param_name[idx+1:]
+            new_param_name = ".".join(new_param_name)
+
+            print(f'Deepspeed {rank=}, renaming {param_name} -> {new_param_name}')
+            state_dict[new_param_name] = state_dict.pop(param_name)
+
+        # rename gate params
+        ds_suffix = "mlp.deepspeed_moe.gate.wg.weight".split('.')
+        for param_name in gate_params:
+            new_param_name = '.'.join(param_name.split('.')[:4] + ds_suffix)
+            print(f'Gating: {rank=}, renaming {param_name} -> {new_param_name}')
+            state_dict[new_param_name] = state_dict.pop(param_name)
+
+        # If deepspeed lora is enabled, then we need to rename weight to base_weight.
+        # Furthermore, if the base_weight is sharded, we need to shard each weight and select the slice of local rank.
+        if self.is_deepspeed_lora:
+            local_state_dict = self.state_dict()
+            for param_name in local_state_dict:
+                if not param_name.endswith("base_weight"):
+                    continue
+
+                incoming_param_name = param_name.replace("base_weight", "weight")
+                if incoming_param_name not in state_dict:
+                    continue
+
+                incoming_param = state_dict[incoming_param_name]
+
+                shape_local = local_state_dict[param_name].shape
+                shape_incoming = incoming_param.shape
+                if 'deepspeed_moe' in incoming_param_name:
+                    assert shape_local == shape_incoming, "deepspeed moe weights are never sharded"
+                else:
+                    assert shape_incoming[1] == shape_local[1] * world_size, "weights should be sharded equally across world size"
+                    incoming_param = incoming_param[:, rank*shape_local[1]: (rank+1)*shape_local[1]]
+                print(f'Deepspeed lora: {rank=}, renaming {incoming_param_name} -> {param_name}')
+                state_dict[param_name] = incoming_param
+                del state_dict[incoming_param_name]
+
+        return super()._load_from_state_dict(
+            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+        )
+
+    @add_start_docstrings_to_model_forward(MIXTRAL_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    # Ignore copy
+    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, MoeCausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                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:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, ArcticForCausalLM
+
+        >>> model = ArcticForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        # Move to same device for model parallelism.
+        aux_loss = sum([out.to(logits.device) for out in outputs[-1]])
+        if labels is not None:
+            loss += self.router_aux_loss_coef * aux_loss
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            # torch.distributed.barrier()
+            return (loss,) + output if loss is not None else output
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            aux_loss=aux_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    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 Arctic Model transformer with a sequence classification head on top (linear layer).
+
+    [`ArcticForSequenceClassification`] 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).
+    """,
+    ARCTIC_START_DOCSTRING,
+)
+# Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with Llama->Arctic, LLAMA->MIXTRAL
+class ArcticForSequenceClassification(ArcticPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = ArcticModel(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(MIXTRAL_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 SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

tokenization_arctic.py

@@ -0,0 +1,57 @@
+"""Tokenization classes for Arctic."""
+
+from typing import Any, Dict, Optional
+
+from transformers.models.llama import LlamaTokenizer
+
+
+class ArcticTokenizer(LlamaTokenizer):
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token=None,
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        clean_up_tokenization_spaces=False,
+        use_default_system_prompt=False,
+        spaces_between_special_tokens=False,
+        legacy=False,
+        add_prefix_space=True,
+        **kwargs,
+    ):
+        # Same as LlamaTokenizer except default legacy=False.
+        super().__init__(
+            vocab_file,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            sp_model_kwargs=sp_model_kwargs,
+            add_bos_token=add_bos_token,
+            add_eos_token=add_eos_token,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            use_default_system_prompt=use_default_system_prompt,
+            spaces_between_special_tokens=spaces_between_special_tokens,
+            legacy=legacy,
+            add_prefix_space=add_prefix_space,
+            **kwargs,
+        )
+
+    @property
+    def default_chat_template(self):
+        """
+        This template formats inputs in the standard Arctic format.
+        """
+        return (
+            "{% for message in messages %}"
+            "{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}"
+            "{% endfor %}"
+            "{% if add_generation_prompt %}"
+            "{{ '<|im_start|>assistant\n' }}"
+            "{% endif %}"
+        )

tokenizer_config.json

@@ -47,6 +47,9 @@
     "<s>",
     "</s>"
   ],
+  "auto_map": {
+    "AutoTokenizer": ["tokenization_arctic.ArcticTokenizer", null]
+  },
   "bos_token": "<|im_start|>",
   "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
   "clean_up_tokenization_spaces": false,