Upload NGMEForCausalLM

config.json

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+{
+  "_name_or_path": "/scratch/phmaker/ngme/babylm",
+  "architectures": [
+    "NGMEForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_ngme.NGMEConfig",
+    "AutoModelForCausalLM": "modeling_ngme.NGMEForCausalLM"
+  },
+  "bos_token_id": 1,
+  "eos_token_id": 0,
+  "ffn_dim": 512,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 2048,
+  "model_type": "ngme",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 4,
+  "num_key_value_heads": 4,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.31.0",
+  "unk_idx": 1,
+  "unk_token_id": 1,
+  "use_cache": true,
+  "use_flash_attn": false,
+  "use_small_embedding": false,
+  "vocab_size": 36484
+}

configuration_ngme.py

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+# coding=utf-8
+# Copyright 2022 EleutherAI 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.
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class NGMEConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`NGMEModel`]. It is used to instantiate an LLaMA
+    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 LLaMA-7B.
+
+    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 LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`NGMEModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            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*):
+            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
+            `num_attention_heads`.
+        pretraining_tp (`int`, *optional*, defaults to `1`):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        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 2048):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        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-12):
+            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`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
+            is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+
+        Example:
+
+    ```python
+    >>> from transformers import NGMEModel, LlamaConfig
+
+    >>> # Initializing a LLaMA llama-7b style configuration
+    >>> configuration = NGMEConfig()
+
+    >>> # Initializing a model from the llama-7b style configuration
+    >>> model = NGMEModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "ngme"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_scaling=None,
+        use_flash_attn=False,
+        use_small_embedding=False,
+        unk_idx=-1,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.unk_idx = unk_idx
+        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
+
+        # 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.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.use_flash_attn = use_flash_attn
+        self.use_small_embedding = use_small_embedding
+
+        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,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 0,
+  "pad_token_id": 0,
+  "transformers_version": "4.31.0"
+}

modeling_ngme.py

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+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from transformers.generation.utils import SampleOutput
+from transformers.generation.logits_process import LogitsProcessorList
+from transformers.generation.stopping_criteria import StoppingCriteriaList
+
+from .tokenization_ngme import NGMETokenizer
+from .sampling import sample as sample_ngme
+from .configuration_ngme import NGMEConfig
+from .ngme import (
+    soft_n_hot,
+    NGramsEmbedding,
+    collect_n_gram_sequences,
+    shift_with_pad,
+)
+
+logger = logging.get_logger(__name__)
+
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+    input_ids_shape: torch.Size,
+    dtype: torch.dtype,
+    device: torch.device,
+    past_key_values_length: int = 0,
+):
+    """
+    Make causal mask used for bi-directional self-attention.
+    """
+    bsz, tgt_len = input_ids_shape
+    mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
+    mask_cond = torch.arange(mask.size(-1), device=device)
+    mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+    mask = mask.to(dtype)
+
+    if past_key_values_length > 0:
+        mask = torch.cat(
+            [
+                torch.zeros(
+                    tgt_len, past_key_values_length, dtype=dtype, device=device
+                ),
+                mask,
+            ],
+            dim=-1,
+        )
+    return mask[None, None, :, :].expand(
+        bsz, 1, tgt_len, tgt_len + past_key_values_length
+    )
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    """
+    Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+    """
+    bsz, src_len = mask.size()
+    tgt_len = tgt_len if tgt_len is not None else src_len
+
+    expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+    inverted_mask = 1.0 - expanded_mask
+
+    return inverted_mask.masked_fill(
+        inverted_mask.to(torch.bool), torch.finfo(dtype).min
+    )
+
+
+class NGMERMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        NGMERMSNorm 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)
+
+
+class NGMERotaryEmbedding(torch.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.einsum("i,j->ij", 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()[None, None, :, :].to(dtype), persistent=False
+        )
+        self.register_buffer(
+            "sin_cached", emb.sin()[None, None, :, :].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),
+        )
+
+
+class NGMELinearScalingRotaryEmbedding(NGMERotaryEmbedding):
+    """NGMERotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
+        )
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum("i,j->ij", 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()[None, None, :, :].to(dtype), persistent=False
+        )
+        self.register_buffer(
+            "sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
+        )
+
+
+class NGMEDynamicNTKScalingRotaryEmbedding(NGMERotaryEmbedding):
+    """NGMERotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        device=None,
+        scaling_factor=1.0,
+    ):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings)
+                - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (
+                base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim)
+            )
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(
+            self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype
+        )
+
+        freqs = torch.einsum("i,j->ij", 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()[None, None, :, :].to(dtype), persistent=False
+        )
+        self.register_buffer(
+            "sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False
+        )
+
+
+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)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
+    # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+    cos = cos.squeeze(1).squeeze(0)  # [seq_len, dim]
+    sin = sin.squeeze(1).squeeze(0)  # [seq_len, dim]
+    cos = cos[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(1)  # [bs, 1, seq_len, dim]
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class NGMEMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        if self.config.pretraining_tp > 1:
+            slice = self.intermediate_size // self.config.pretraining_tp
+            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+            down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+            gate_proj = torch.cat(
+                [
+                    F.linear(x, gate_proj_slices[i])
+                    for i in range(self.config.pretraining_tp)
+                ],
+                dim=-1,
+            )
+            up_proj = torch.cat(
+                [
+                    F.linear(x, up_proj_slices[i])
+                    for i in range(self.config.pretraining_tp)
+                ],
+                dim=-1,
+            )
+
+            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+            down_proj = [
+                F.linear(intermediate_states[i], down_proj_slices[i])
+                for i in range(self.config.pretraining_tp)
+            ]
+            down_proj = sum(down_proj)
+        else:
+            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+        return down_proj
+
+
+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)
+
+
+class NGMEAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: NGMEConfig):
+        super().__init__()
+        self.config = config
+        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
+
+        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})."
+            )
+        self.q_proj = nn.Linear(
+            self.hidden_size, self.num_heads * self.head_dim, bias=False
+        )
+        self.k_proj = nn.Linear(
+            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
+        )
+        self.v_proj = nn.Linear(
+            self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False
+        )
+        self.o_proj = nn.Linear(
+            self.num_heads * self.head_dim, self.hidden_size, bias=False
+        )
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = NGMERotaryEmbedding(
+                self.head_dim, max_position_embeddings=self.max_position_embeddings
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = NGMELinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = NGMEDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return (
+            tensor.view(bsz, seq_len, self.num_heads, self.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[Tuple[torch.Tensor]] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        if self.config.pretraining_tp > 1:
+            key_value_slicing = (
+                self.num_key_value_heads * self.head_dim
+            ) // self.config.pretraining_tp
+            query_slices = self.q_proj.weight.split(
+                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
+            )
+            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+            query_states = [
+                F.linear(hidden_states, query_slices[i])
+                for i in range(self.config.pretraining_tp)
+            ]
+            query_states = torch.cat(query_states, dim=-1)
+
+            key_states = [
+                F.linear(hidden_states, key_slices[i])
+                for i in range(self.config.pretraining_tp)
+            ]
+            key_states = torch.cat(key_states, dim=-1)
+
+            value_states = [
+                F.linear(hidden_states, value_slices[i])
+                for i in range(self.config.pretraining_tp)
+            ]
+            value_states = torch.cat(value_states, dim=-1)
+
+        else:
+            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[0].shape[-2]
+        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:
+            # reuse k, v, self_attention
+            key_states = torch.cat([past_key_value[0], key_states], dim=2)
+            value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+        past_key_value = (key_states, value_states) if use_cache else None
+
+        # 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_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)
+
+        if self.config.pretraining_tp > 1:
+            attn_output = attn_output.split(
+                self.hidden_size // self.config.pretraining_tp, dim=2
+            )
+            o_proj_slices = self.o_proj.weight.split(
+                self.hidden_size // self.config.pretraining_tp, dim=1
+            )
+            attn_output = sum(
+                [
+                    F.linear(attn_output[i], o_proj_slices[i])
+                    for i in range(self.config.pretraining_tp)
+                ]
+            )
+        else:
+            attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class NGMEDecoderLayer(nn.Module):
+    def __init__(self, config: NGMEConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = NGMEAttention(config=config)
+        self.mlp = NGMEMLP(config)
+        self.input_layernorm = NGMERMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = NGMERMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+    ) -> Tuple[
+        torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
+    ]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative 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.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+class NGMEPreTrainedModel(PreTrainedModel):
+    config_class = NGMEConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["NGMEDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, NGramsEmbedding):
+            if self.config.use_small_embedding:
+                nn.init.uniform_(module.weight, a=-1e-4, b=1e-4)
+            else:
+                module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, NGMEModel):
+            module.gradient_checkpointing = value
+
+
+class NGMEModel(NGMEPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`NGMEDecoderLayer`]
+
+    Args:
+        config: NGMEConfig
+    """
+
+    def __init__(self, config: NGMEConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = NGramsEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            self.padding_idx,
+            unk_idx=config.unk_idx,
+        )
+
+        if self.config.use_small_embedding:
+            self.embed_layer_norm = nn.LayerNorm(config.hidden_size)
+
+        self.layers = nn.ModuleList(
+            [NGMEDecoderLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.norm = NGMERMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+    def _prepare_decoder_attention_mask(
+        self, attention_mask, input_shape, inputs_embeds, past_key_values_length
+    ):
+        # create causal mask
+        # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+        combined_attention_mask = None
+        if input_shape[-1] > 1:
+            combined_attention_mask = _make_causal_mask(
+                input_shape,
+                inputs_embeds.dtype,
+                device=inputs_embeds.device,
+                past_key_values_length=past_key_values_length,
+            )
+
+        if attention_mask is not None:
+            # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+            expanded_attn_mask = _expand_mask(
+                attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
+            ).to(inputs_embeds.device)
+            combined_attention_mask = (
+                expanded_attn_mask
+                if combined_attention_mask is None
+                else expanded_attn_mask + combined_attention_mask
+            )
+
+        return combined_attention_mask
+
+    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,
+        ngram_sequences: List[torch.Tensor] = [],
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        # retrieve input_ids and inputs_embeds
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time"
+            )
+        elif input_ids is not None:
+            batch_size, seq_length = input_ids.shape
+        elif inputs_embeds is not None:
+            batch_size, seq_length, _ = inputs_embeds.shape
+        else:
+            raise ValueError(
+                "You have to specify either decoder_input_ids or decoder_inputs_embeds"
+            )
+
+        seq_length_with_past = seq_length
+        past_key_values_length = 0
+
+        if past_key_values is not None:
+            past_key_values_length = past_key_values[0][0].shape[2]
+            seq_length_with_past = seq_length_with_past + past_key_values_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, ngram_sequences)
+
+            if self.config.use_small_embedding:
+                inputs_embeds = self.embed_layer_norm(inputs_embeds)
+
+        # embed positions
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                (batch_size, seq_length_with_past),
+                dtype=torch.bool,
+                device=inputs_embeds.device,
+            )
+        attention_mask = self._prepare_decoder_attention_mask(
+            attention_mask,
+            (batch_size, seq_length),
+            inputs_embeds,
+            past_key_values_length,
+        )
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning_once(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = () if use_cache else None
+
+        for idx, decoder_layer in enumerate(self.layers):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            past_key_value = (
+                past_key_values[idx] if past_key_values is not None else None
+            )
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for past_key_value
+                        return module(*inputs, output_attentions, None)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(decoder_layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    None,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    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 = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if not return_dict:
+            return tuple(
+                v
+                for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
+                if v is not None
+            )
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class NGMEForCausalLM(NGMEPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = NGMEModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.tokenizer: Optional[NGMETokenizer] = None
+        weight = torch.ones(config.vocab_size)
+        weight[config.unk_idx] = 0
+        self.loss_fct = CrossEntropyLoss(weight=weight)
+
+        # Create weight tensor for the vocab weights and ignore unk_token weights
+        weigth_tensor = torch.ones(config.vocab_size)
+        weigth_tensor[config.unk_idx] = 0
+        self.loss_fct = nn.CrossEntropyLoss(weight=weigth_tensor)
+
+        # 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 _collect_ngram_labels(
+        self,
+        unigram_labels: torch.LongTensor,
+        label_gram_2_sequence: Optional[torch.LongTensor] = None,
+        label_gram_3_sequence: Optional[torch.LongTensor] = None,
+        label_gram_4_sequence: Optional[torch.LongTensor] = None,
+        label_target_gram_2_sequence: Optional[torch.LongTensor] = None,
+        label_target_gram_3_sequence: Optional[torch.LongTensor] = None,
+        label_target_gram_4_sequence: Optional[torch.LongTensor] = None,
+    ):
+        ngram_labels = [unigram_labels[..., 1:].contiguous()]
+
+        if label_gram_2_sequence is not None:
+            if label_target_gram_2_sequence is not None:
+                two_gram_labels = label_target_gram_2_sequence[..., 1:].contiguous()
+            else:
+                two_gram_labels = shift_with_pad(
+                    label_gram_2_sequence, 2, unigram_labels
+                )
+            ngram_labels.append(two_gram_labels)
+
+        if label_gram_3_sequence is not None:
+            if label_target_gram_3_sequence is not None:
+                three_gram_labels = label_target_gram_3_sequence[..., 1:].contiguous()
+            else:
+                three_gram_labels = shift_with_pad(
+                    label_gram_3_sequence, 3, unigram_labels
+                )
+            ngram_labels.append(three_gram_labels)
+
+        if label_gram_4_sequence is not None:
+            if label_target_gram_4_sequence is not None:
+                four_gram_labels = label_target_gram_4_sequence[..., 1:].contiguous()
+            else:
+                four_gram_labels = shift_with_pad(
+                    label_gram_4_sequence, 4, unigram_labels
+                )
+            ngram_labels.append(four_gram_labels)
+
+        return ngram_labels
+
+    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,
+        label_gram_2_sequence: Optional[torch.LongTensor] = None,
+        label_gram_3_sequence: Optional[torch.LongTensor] = None,
+        label_gram_4_sequence: Optional[torch.LongTensor] = None,
+        label_target_gram_2_sequence: Optional[torch.LongTensor] = None,
+        label_target_gram_3_sequence: Optional[torch.LongTensor] = None,
+        label_target_gram_4_sequence: Optional[torch.LongTensor] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        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
+        )
+
+        ngram_sequences = collect_n_gram_sequences(
+            gram_2_sequence=label_gram_2_sequence,
+            gram_3_sequence=label_gram_3_sequence,
+            gram_4_sequence=label_gram_4_sequence,
+        )
+
+        # 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,
+            ngram_sequences=ngram_sequences,
+        )
+
+        hidden_states = outputs[0]
+        if self.config.pretraining_tp > 1:
+            lm_head_slices = self.lm_head.weight.split(
+                self.vocab_size // self.config.pretraining_tp, dim=0
+            )
+            logits = [
+                F.linear(hidden_states, lm_head_slices[i])
+                for i in range(self.config.pretraining_tp)
+            ]
+            logits = torch.cat(logits, dim=-1)
+        else:
+            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()
+
+            ngram_labels = self._collect_ngram_labels(
+                unigram_labels=labels,
+                label_gram_2_sequence=label_gram_2_sequence,
+                label_gram_3_sequence=label_gram_3_sequence,
+                label_gram_4_sequence=label_gram_4_sequence,
+                label_target_gram_2_sequence=label_target_gram_2_sequence,
+                label_target_gram_3_sequence=label_target_gram_3_sequence,
+                label_target_gram_4_sequence=label_target_gram_4_sequence,
+            )
+
+            shift_labels = torch.stack(ngram_labels, dim=0)
+            shift_labels = soft_n_hot(
+                shift_labels, self.config.vocab_size, strategy="exp"
+            )
+
+            # Flatten the tokens
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1, shift_labels.size(-1))
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = self.loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        **kwargs,
+    ):
+        if past_key_values:
+            input_ids = input_ids[:, -1:]
+
+        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[:, -1].unsqueeze(-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
+
+    def sample(
+        self,
+        input_ids: torch.LongTensor,
+        logits_processor: Optional[LogitsProcessorList] = None,
+        stopping_criteria: Optional[StoppingCriteriaList] = None,
+        logits_warper: Optional[LogitsProcessorList] = None,
+        max_length: Optional[int] = None,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[Union[int, List[int]]] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        output_scores: Optional[bool] = None,
+        return_dict_in_generate: Optional[bool] = None,
+        synced_gpus: bool = False,
+        streamer=None,
+        **model_kwargs,
+    ) -> Union[SampleOutput, torch.LongTensor]:
+        if not hasattr(self, "tokenizer"):
+            raise ValueError(
+                "You are trying to sample from a model that does not have a tokenizer."
+                "Add a tokenizer as an attribute of your model (either manually or automatically)."
+            )
+
+        return sample_ngme(
+            self,
+            input_ids,
+            logits_processor=logits_processor,
+            stopping_criteria=stopping_criteria,
+            logits_warper=logits_warper,
+            max_length=max_length,
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_scores=output_scores,
+            return_dict_in_generate=return_dict_in_generate,
+            synced_gpus=synced_gpus,
+            streamer=streamer,
+            **model_kwargs,
+        )
+
+
+class NGMEForSequenceClassification(NGMEPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = NGMEModel(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
+
+    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,
+        label_gram_2_sequence: Optional[torch.LongTensor] = None,
+        label_gram_3_sequence: Optional[torch.LongTensor] = None,
+        label_gram_4_sequence: Optional[torch.LongTensor] = 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
+        )
+
+        ngram_sequences = collect_n_gram_sequences(
+            gram_2_sequence=label_gram_2_sequence,
+            gram_3_sequence=label_gram_3_sequence,
+            gram_4_sequence=label_gram_4_sequence,
+        )
+
+        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,
+            ngram_sequences=ngram_sequences,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError(
+                "Cannot handle batch sizes > 1 if no padding token is defined."
+            )
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                sequence_lengths = (
+                    torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1
+                ).to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[
+            torch.arange(batch_size, device=logits.device), sequence_lengths
+        ]
+
+        loss = None
+
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (
+                    labels.dtype == torch.long or labels.dtype == torch.int
+                ):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(
+                    pooled_logits.view(-1, self.num_labels), labels.view(-1)
+                )
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )

ngme.py

@@ -0,0 +1,178 @@
+import math
+from typing import Optional, List
+from functools import lru_cache
+from itertools import chain, tee
+
+import torch
+import torch.nn.functional as F
+
+n_dists = {
+    0: [1],
+    1: [0.4, 0.6],
+    2: [0.2, 0.3, 0.5],
+    3: [0.1, 0.2, 0.3, 0.4],
+    4: [0.1, 0.15, 0.2, 0.25, 0.3],
+}
+
+strats = {"linear": lambda x: x, "log": lambda x: math.log(x + 1), "exp": lambda x: x**2}
+
+def pad_sequence(
+    sequence,
+    n,
+    pad_left=False,
+    pad_right=False,
+    left_pad_symbol=None,
+    right_pad_symbol=None,
+):
+    """Copied from NLTK"""
+    sequence = iter(sequence)
+    if pad_left:
+        sequence = chain((left_pad_symbol,) * (n - 1), sequence)
+    if pad_right:
+        sequence = chain(sequence, (right_pad_symbol,) * (n - 1))
+    return sequence
+
+def ngrams(sequence, n, **kwargs):
+    """Copied from NLTK"""
+    sequence = pad_sequence(sequence, n, **kwargs)
+
+    # Creates the sliding window, of n no. of items.
+    # `iterables` is a tuple of iterables where each iterable is a window of n items.
+    iterables = tee(sequence, n)
+
+    for i, sub_iterable in enumerate(iterables):  # For each window,
+        for _ in range(i):  # iterate through every order of ngrams
+            next(sub_iterable, None)  # generate the ngrams within the window.
+    return zip(*iterables)  # Unpack and flattens the iterables.
+
+
+@lru_cache(maxsize=5)
+def soft_dist(n):
+    return [1 / n] * n
+
+
+@lru_cache(maxsize=5)
+def n_dist(n: int, strategy: str) -> list[float]:
+    """dist of ngram weight is logarithmic"""
+    ns = list(range(1, n + 1))
+    xs = list(map(strats[strategy], ns))
+    result = list(map(lambda x: x / sum(xs), xs))
+    return result
+
+def soft_n_hot(
+    input,
+    num_classes: int,
+    strategy: Optional[str],
+):
+
+    shape = list(input.size())[1:]
+
+    shape.append(num_classes)
+
+    ret = torch.zeros(shape).to(input.device)
+
+    if strategy:
+        soft_labels = n_dist(input.size(0), strategy)
+    else:
+        soft_labels = [1] * input.size(0)
+
+    for i, t in enumerate(input):
+        ret.scatter_(-1, t.unsqueeze(-1), soft_labels[i])
+
+    return ret
+
+
+def n_hot(t, num_clases, ngram_sequences: Optional[torch.Tensor] = None, unk_idx: Optional[int] = None):
+
+    shape = list(t.size())
+
+    if ngram_sequences is not None:
+        shape.append(num_clases)
+        ret = torch.zeros(shape).to(t.device)
+        ret.scatter_(-1, t.unsqueeze(-1), 1)
+        for seq in ngram_sequences:
+            if unk_idx is not None:
+                mask = torch.eq(seq, unk_idx)
+                seq[mask] = t[mask]
+            ret.scatter_(-1, seq.unsqueeze(-1), 1)
+        return ret
+
+    elif len(shape) == 2:
+        return F.one_hot(t, num_classes=num_clases).float()
+    else:
+        shape = shape[1:]
+        shape.append(num_clases)
+        ret = torch.zeros(shape).to(t.device)
+        # Expect that first dimension is for all n-grams
+        for seq in t:
+            ret.scatter_(-1, seq.unsqueeze(-1), 1)
+
+    return ret
+
+
+class NGramsEmbedding(torch.nn.Embedding):
+    """N-Hot encoder"""
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: Optional[int] = None,
+        max_norm: Optional[float] = None,
+        norm_type: float = 2,
+        scale_grad_by_freq: bool = False,
+        sparse: bool = False,
+        _weight: Optional[torch.Tensor] = None,
+        device=None,
+        dtype=None,
+        unk_idx: Optional[int] = None
+    ) -> None:
+        super().__init__(
+            num_embeddings,
+            embedding_dim,
+            padding_idx=padding_idx,
+            max_norm=max_norm,
+            norm_type=norm_type,
+            scale_grad_by_freq=scale_grad_by_freq,
+            sparse=sparse,
+            _weight=_weight,
+            device=device,
+            dtype=dtype,
+        )
+
+        self.num_classes = num_embeddings
+        self.unk_idx = unk_idx
+
+    def forward(self, input: torch.Tensor, ngram_sequences: Optional[torch.Tensor] = None):
+        return self._forward(
+            n_hot(input, self.num_classes, ngram_sequences, self.unk_idx)
+        )
+
+    def _forward(self, n_hot: torch.Tensor) -> torch.Tensor:
+        return F.linear(n_hot, self.weight.t())
+
+
+def collect_n_gram_sequences(**kwargs) -> List[torch.Tensor]:
+    sequences = []
+    for n in range(2, len(kwargs)+2):
+        s = kwargs[f"gram_{n}_sequence"]
+        if s is not None:
+            sequences.append(s)
+        else:
+            break
+
+    return sequences
+
+def shift_with_pad(target_tensor, n, from_tensor):
+    shifted = target_tensor[:, n:]
+
+    seq_size = target_tensor.size(1) - 1
+
+    missing_idxs = torch.arange(seq_size - (n-1), seq_size).to(target_tensor.device)
+
+    # Pad with missing idxs from unigram tensor
+    shifted = torch.concat(
+        (shifted, from_tensor.index_select(1, missing_idxs)), dim=1
+    )
+
+    return shifted

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b26375a83551a0a47ff2fdc26e2ca3616198d3c3a12e5b5ef4ea70ac51f9296
+size 907246313

sampling.py

@@ -0,0 +1,205 @@
+import warnings
+from typing import List, Optional, Union
+
+import torch
+import torch.distributed as dist
+from torch import nn
+from transformers import BatchEncoding
+from transformers.generation.logits_process import (
+    LogitsProcessorList,
+)
+from transformers.generation.stopping_criteria import (
+    StoppingCriteriaList,
+    validate_stopping_criteria,
+)
+
+from transformers.generation.utils import SampleOutput, SampleEncoderDecoderOutput, SampleDecoderOnlyOutput
+
+def sample(
+    self,
+    input_ids: torch.LongTensor,
+    logits_processor: Optional[LogitsProcessorList] = None,
+    stopping_criteria: Optional[StoppingCriteriaList] = None,
+    logits_warper: Optional[LogitsProcessorList] = None,
+    max_length: Optional[int] = None,
+    pad_token_id: Optional[int] = None,
+    eos_token_id: Optional[Union[int, List[int]]] = None,
+    output_attentions: Optional[bool] = None,
+    output_hidden_states: Optional[bool] = None,
+    output_scores: Optional[bool] = None,
+    return_dict_in_generate: Optional[bool] = None,
+    synced_gpus: Optional[bool] = False,
+    **model_kwargs,
+) -> Union[SampleOutput, torch.LongTensor]:
+
+    if type(input_ids) in [dict, BatchEncoding]:
+        input_ids, ngram_sequences = input_ids["input_ids"], input_ids
+        del ngram_sequences["input_ids"]
+        del ngram_sequences["attention_mask"]
+    else:
+        ngram_sequences = {}
+
+    # init values
+    logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+    stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+    if max_length is not None:
+        warnings.warn(
+            "`max_length` is deprecated in this function, use"
+            " `stopping_criteria=StoppingCriteriaList(MaxLengthCriteria(max_length=max_length))` instead.",
+            UserWarning,
+        )
+        stopping_criteria = validate_stopping_criteria(stopping_criteria, max_length)
+    logits_warper = logits_warper if logits_warper is not None else LogitsProcessorList()
+    pad_token_id = pad_token_id if pad_token_id is not None else self.generation_config.pad_token_id
+    eos_token_id = eos_token_id if eos_token_id is not None else self.generation_config.eos_token_id
+    if isinstance(eos_token_id, int):
+        eos_token_id = [eos_token_id]
+
+    eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
+    output_scores = output_scores if output_scores is not None else self.generation_config.output_scores
+    output_attentions = (
+        output_attentions if output_attentions is not None else self.generation_config.output_attentions
+    )
+    output_hidden_states = (
+        output_hidden_states if output_hidden_states is not None else self.generation_config.output_hidden_states
+    )
+    return_dict_in_generate = (
+        return_dict_in_generate
+        if return_dict_in_generate is not None
+        else self.generation_config.return_dict_in_generate
+    )
+
+    # init attention / hidden states / scores tuples
+    scores = () if (return_dict_in_generate and output_scores) else None
+    decoder_attentions = () if (return_dict_in_generate and output_attentions) else None
+    cross_attentions = () if (return_dict_in_generate and output_attentions) else None
+    decoder_hidden_states = () if (return_dict_in_generate and output_hidden_states) else None
+
+    # if model is an encoder-decoder, retrieve encoder attention weights and hidden states
+    if return_dict_in_generate and self.config.is_encoder_decoder:
+        encoder_attentions = model_kwargs["encoder_outputs"].get("attentions") if output_attentions else None
+        encoder_hidden_states = (
+            model_kwargs["encoder_outputs"].get("hidden_states") if output_hidden_states else None
+        )
+
+    # keep track of which sequences are already finished
+    unfinished_sequences = torch.ones(input_ids.shape[0], dtype=torch.long, device=input_ids.device)
+
+    this_peer_finished = False  # used by synced_gpus only
+    # auto-regressive generation
+    while True:
+        if synced_gpus:
+            # Under synced_gpus the `forward` call must continue until all gpus complete their sequence.
+            # The following logic allows an early break if all peers finished generating their sequence
+            this_peer_finished_flag = torch.tensor(0.0 if this_peer_finished else 1.0).to(input_ids.device)
+            # send 0.0 if we finished, 1.0 otherwise
+            dist.all_reduce(this_peer_finished_flag, op=dist.ReduceOp.SUM)
+            # did all peers finish? the reduced sum will be 0.0 then
+            if this_peer_finished_flag.item() == 0.0:
+                break
+
+        # prepare model inputs
+        model_inputs = {"input_ids": input_ids}
+
+        # forward pass to get next token
+        outputs = self(
+            **model_inputs,
+            return_dict=True,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            **ngram_sequences
+        )
+
+        if synced_gpus and this_peer_finished:
+            continue  # don't waste resources running the code we don't need
+
+        next_token_logits = outputs.logits[:, -1, :]
+
+        # pre-process distribution
+        next_token_scores = logits_processor(input_ids, next_token_logits)
+        next_token_scores = logits_warper(input_ids, next_token_scores)
+
+        # Store scores, attentions and hidden_states when required
+        if return_dict_in_generate:
+            if output_scores:
+                scores += (next_token_scores,)
+            if output_attentions:
+                decoder_attentions += (
+                    (outputs.decoder_attentions,) if self.config.is_encoder_decoder else (outputs.attentions,)
+                )
+                if self.config.is_encoder_decoder:
+                    cross_attentions += (outputs.cross_attentions,)
+
+            if output_hidden_states:
+                decoder_hidden_states += (
+                    (outputs.decoder_hidden_states,)
+                    if self.config.is_encoder_decoder
+                    else (outputs.hidden_states,)
+                )
+
+        # sample
+        probs = nn.functional.softmax(next_token_scores, dim=-1)
+        next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+
+        # finished sentences should have their next token be a padding token
+        if eos_token_id is not None:
+            if pad_token_id is None:
+                raise ValueError("If `eos_token_id` is defined, make sure that `pad_token_id` is defined.")
+            next_tokens = next_tokens * unfinished_sequences + pad_token_id * (1 - unfinished_sequences)
+
+        # update generated ids, model inputs, and length for next step
+        input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+        decoded = self.tokenizer.batch_decode(input_ids)[0]
+        encoded = self.tokenizer(
+            decoded, return_tensors="pt", return_ngram_sequences=True
+        )
+        input_ids = encoded.input_ids.to(self.device)
+
+        ngram_sequences = {}
+
+        if "label_gram_2_sequence" in encoded:
+            ngram_sequences["label_gram_2_sequence"] = encoded["label_gram_2_sequence"].to(self.device)
+
+        if "label_gram_3_sequence" in encoded:
+            ngram_sequences["label_gram_3_sequence"] = encoded["label_gram_3_sequence"].to(self.device)
+
+        if "label_gram_4_sequence" in encoded:
+            ngram_sequences["label_gram_4_sequence"] = encoded["label_gram_4_sequence"].to(self.device)
+
+        model_kwargs = self._update_model_kwargs_for_generation(
+            outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
+        )
+
+        # if eos_token was found in one sentence, set sentence to finished
+        if eos_token_id_tensor is not None:
+            unfinished_sequences = unfinished_sequences.mul(
+                next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
+            )
+
+        # stop when each sentence is finished, or if we exceed the maximum length
+        if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
+            if not synced_gpus:
+                break
+            else:
+                this_peer_finished = True
+
+    if return_dict_in_generate:
+        if self.config.is_encoder_decoder:
+            return SampleEncoderDecoderOutput(
+                sequences=input_ids,
+                scores=scores,
+                encoder_attentions=encoder_attentions,
+                encoder_hidden_states=encoder_hidden_states,
+                decoder_attentions=decoder_attentions,
+                cross_attentions=cross_attentions,
+                decoder_hidden_states=decoder_hidden_states,
+            )
+        else:
+            return SampleDecoderOnlyOutput(
+                sequences=input_ids,
+                scores=scores,
+                attentions=decoder_attentions,
+                hidden_states=decoder_hidden_states,
+            )
+    else:
+        return input_ids

tokenization_ngme.py

@@ -0,0 +1,1299 @@
+import json
+import unittest
+import os
+from collections import Counter
+from typing import Dict, List, Optional, Sized, Tuple, Union, Any
+
+import torch
+import numpy as np
+from tokenizers import AddedToken
+from transformers import PreTrainedTokenizer
+from transformers.tokenization_utils_base import (
+    BatchEncoding,
+    EncodedInput,
+    TruncationStrategy,
+)
+from transformers.utils import logging
+from transformers.utils.generic import PaddingStrategy, TensorType, to_py_obj
+
+from .ngme import ngrams as ngram_tokenizer
+
+logger = logging.get_logger(__name__)
+
+
+def load_vocab(vocab_file):
+    """Loads a vocabulary file into a dictionary."""
+    with open(vocab_file, "r", encoding="utf-8") as f:
+        vocab = json.load(f)
+    return vocab
+
+
+def all_same(items):
+    return all(x == items[0] for x in items)
+
+
+class NGMETokenizer(PreTrainedTokenizer):
+    model_input_names = ["input_ids", "attention_mask"]
+    vocab_file = "vocab.json"
+    vocab_files_names = {"vocab_file": vocab_file}
+
+    def __init__(
+        self,
+        vocab_file,
+        eos_token="\n",
+        pad_token="\n",
+        unk_token="<unk>",
+        eod_token="<eod>",
+        **kwargs,
+    ):
+        super().__init__(
+            eos_token=eos_token, pad_token=pad_token, unk_token=unk_token, **kwargs
+        )
+
+        eos_token = (
+            AddedToken(
+                eos_token,
+                lstrip=False,
+                rstrip=False,
+            )
+            if isinstance(eos_token, str)
+            else eos_token
+        )
+        pad_token = (
+            AddedToken(
+                pad_token,
+                lstrip=False,
+                rstrip=False,
+            )
+            if isinstance(pad_token, str)
+            else pad_token
+        )
+        unk_token = (
+            AddedToken(
+                unk_token,
+                lstrip=False,
+                rstrip=False,
+            )
+            if isinstance(unk_token, str)
+            else unk_token
+        )
+
+        self._ngram2word2idx = {}
+        self._ngram2idx2word = {}
+        self._current_max_idx = 0
+        self._frequencies: Counter = Counter()
+
+        self._load_from_file(vocab_file)
+
+        for n in range(2, self.ngram + 1):
+            self.model_input_names.append(f"ngram_{n}_sequence")
+
+        # TODO: COuld also be whitespace if n+1gram dont contain it
+        self._special_token = "Ġ"
+        assert self._special_token not in self._ngram2word2idx[1]
+
+    def __call__(self, *args, **kwargs) -> BatchEncoding:
+        if "return_ngram_sequences" in kwargs:
+            return_ngram_sequences = kwargs["return_ngram_sequences"]
+            del kwargs["return_ngram_sequences"]
+        else:
+            return_ngram_sequences = False
+
+        # We could check the args and kwargs beforehand and apply extra ngram sequences based on it, but
+        # we let HF handle all logic and reverse take the char sequence from the ids
+        batch_encoding = super().__call__(*args, **kwargs)
+
+        if return_ngram_sequences:
+            ngram_sequences = self.create_ngram_sequences(args[0])
+            # NOTE: This is pretty hard coded, lets just throw an error if the user wants to use it differently
+
+            if "padding" in kwargs:
+                if kwargs["padding"] == "max_length":
+                    padded_sequences = {}
+                    for n_key, sequence in ngram_sequences.items():
+                        padded_sequences[n_key] = self.pad_sequence_right(
+                            sequence,
+                            len(batch_encoding["input_ids"][0]),
+                            self.pad_token_id,
+                        )
+
+                    ngram_sequences = padded_sequences
+                elif kwargs["padding"] == "longest":
+                    padded_sequences = {}
+                    for n_key, sequence in ngram_sequences.items():
+                        padded_sequences[n_key] = self.pad_sequence_right(
+                            sequence,
+                            max([len(seq) for seq in sequence]),
+                            self.pad_token_id,
+                        )
+                    ngram_sequences = padded_sequences
+
+                else:
+                    raise ValueError(
+                        f"Padding {kwargs['padding']} not supported for ngram sequences"
+                    )
+
+            if "truncation" in kwargs and kwargs["truncation"]:
+                truncated_sequences = {}
+                for n_key, sequence in ngram_sequences.items():
+                    truncated_sequences[n_key] = self.truncate_sequence_right(
+                        sequence, len(batch_encoding["input_ids"][0])
+                    )
+                ngram_sequences = truncated_sequences
+
+            batch_encoding.update(ngram_sequences)
+
+        if "return_tensors" in kwargs:
+            batch_encoding.convert_to_tensors(kwargs["return_tensors"])
+
+        return batch_encoding
+
+    def pad_sequence_right(
+        self, batched_sequence: List[List[int]], padding_length: int, padding_value: int
+    ) -> List[List[int]]:
+        padded_sequence = []
+        for sequence in batched_sequence:
+            padded_sequence.append(
+                sequence + [padding_value] * (padding_length - len(sequence))
+            )
+        return padded_sequence
+
+    def truncate_sequence_right(
+        self, batched_sequence: List[List[int]], max_length: int
+    ) -> List[List[int]]:
+        truncated_sequence = []
+        for sequence in batched_sequence:
+            truncated_sequence.append(sequence[:max_length])
+        return truncated_sequence
+
+    def create_ngram_sequences(self, char_sequences: List[str]) -> Dict[str, Any]:
+        ngram_sequences_output = {}
+
+        if isinstance(char_sequences, str):
+            char_sequences = [char_sequences]
+
+        for n in range(2, self.ngram + 1):
+            ngram_sequences = []
+            for char_sequence in char_sequences:
+                ngrams = ["".join(ngram) for ngram in ngram_tokenizer(char_sequence, n)]
+                # Fill in the front with existign unigrams, for same length and
+                # because the timestep t should not look ahead
+                ngrams = list(char_sequence[: n - 1]) + ngrams
+                encoded_ngrams = self.encode(ngrams) if len(ngrams) > 0 else []
+                ngram_sequences.append(encoded_ngrams)
+
+            ngram_sequences_output[f"label_gram_{n}_sequence"] = ngram_sequences
+
+        return ngram_sequences_output
+
+    def _seq_size(self, encoded) -> Union[int, List[int]]:
+        if isinstance(encoded, torch.Tensor):
+            encoded = encoded.tolist()
+
+        if isinstance(encoded[0], list):
+            return [len(enc) for enc in encoded]
+
+        return len(encoded)
+
+    def _load_from_file(self, filename: str):
+        """Loads a dictionary from a file."""
+        vocab_file = load_vocab(filename)
+        self.ngram = vocab_file["ngram"]
+
+        if "\n" not in vocab_file["vocab"]:
+            self._add_ngram("\n", 1)
+
+        for token in vocab_file["vocab"]:
+            self._add_ngram(token["token"], token["ngram"])
+            self._frequencies.update({token["token"]: token["frequency"]})
+
+    def _add_ngram(self, word, ngram: int) -> int:
+        """Add a new n-gram token to the dictionary."""
+        self._frequencies.update({word: 1})
+
+        if ngram not in self._ngram2idx2word:
+            self._ngram2idx2word[ngram] = {self._current_max_idx: word}
+            self._ngram2word2idx[ngram] = {word: self._current_max_idx}
+            self._current_max_idx += 1
+        else:
+            if word not in self._ngram2word2idx[ngram]:
+                self._ngram2idx2word[ngram][self._current_max_idx] = word
+                self._ngram2word2idx[ngram][word] = self._current_max_idx
+                self._current_max_idx += 1
+
+        return self._ngram2word2idx[ngram][word]
+
+    def _is_contiguous(self):
+        vocab_size = len(self)
+        return list(range(vocab_size)) == [idx for idx, token in self._get_all_tokens()]
+
+    def _get_all_tokens(self):
+        """Returns all tokens in the dictionary."""
+        for ngram in range(1, self.ngram + 1):
+            for idx, token in self._ngram2idx2word[ngram].items():
+                yield idx, token
+
+    def save_vocabulary(
+        self, save_directory: str, filename_prefix: Optional[str] = None
+    ) -> Tuple[str]:
+        filename = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else ""),
+            self.vocab_file,
+        )
+
+        index = 0
+        vocab = {"ngram": self.ngram, "vocab": []}
+
+        for ngram in range(1, self.ngram + 1):
+            for idx, token in self._ngram2idx2word[ngram].items():
+                if index != idx:
+                    index = idx
+
+                try:
+                    frequency = self._frequencies[token]
+                except KeyError:
+                    frequency = -1
+
+                index += 1
+                vocab["vocab"].append(
+                    {
+                        "token": token,
+                        "index": idx,
+                        "frequency": frequency,
+                        "ngram": ngram,
+                    }
+                )
+
+        with open(filename, "w", encoding="utf-8") as writer:
+            json.dump(vocab, writer, indent=4, ensure_ascii=False)
+
+        return (filename,)
+
+    @property
+    def vocab_size(self) -> int:
+        return self._current_max_idx
+
+    def _tokenize(self, text: str) -> List[str]:
+        return list(text)
+
+    def get_idx(self, token: str, ngram: Optional[int] = None) -> int:
+        if ngram:
+            if token in self._ngram2word2idx[ngram]:
+                return self._ngram2word2idx[ngram][token]
+            else:
+                return self._ngram2word2idx[1]["<unk>"]
+
+        for ngram in range(1, self.ngram + 1):
+            if token in self._ngram2word2idx[ngram]:
+                return self._ngram2word2idx[ngram][token]
+
+        return self._ngram2word2idx[1]["<unk>"]
+
+    def _convert_ngram_tokens_to_ids(self, ngram_tokens: List[str]) -> List[int]:
+        return [self.get_idx(token) for token in ngram_tokens]
+
+    def convert_tokens_to_ids(self, tokens: List[str]):
+        if not tokens:
+            return []
+
+        if isinstance(tokens, str):
+            return self.get_idx(tokens)
+
+        return self._convert_ngram_tokens_to_ids(tokens)
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self.get_item_for_index(index)
+
+    def get_item_for_index(self, idx) -> str:
+        """Return the token for a given index."""
+        for idxs in self._ngram2idx2word.values():
+            if idx in idxs:
+                return idxs[idx]
+
+        return self.unk_token
+
+    def convert_tokens_to_string(self, tokens):
+        return "".join(tokens)
+
+    def create_weight_tensor(self) -> torch.Tensor:
+        unked_freqs = self._frequencies.most_common()
+
+        t = torch.ones(len(self))
+
+        for token, freq in unked_freqs:
+            t[self._ngram2word2idx[self._token_to_n_order(token)][token]] = freq
+
+        # Ensure the only whitespace character is weighted
+        t[self._ngram2word2idx[1][" "]] = 1.0
+
+        max_t = max(t)
+
+        normed_weights = torch.tensor([(1 - (x / (max_t + 1))).item() for x in t])
+
+        marker_tokens = [self.get_idx("<unk>", n) for n in range(1, self.ngram + 1)]
+        marker_tokens.extend(
+            [self.get_idx("<start>", n) for n in range(1, self.ngram + 1)]
+        )
+        # Instead of explicit ignore indexes, we use the weight vector and set target idxs to 0
+        for marker in marker_tokens:
+            normed_weights[marker] = 0
+
+        return normed_weights
+
+    def _token_to_n_order(self, token: str) -> int:
+        """Get N-gram order for a token"""
+        for n_gram, word2idx in self._ngram2word2idx.items():
+            if token in word2idx:
+                return n_gram
+
+        return 0
+
+
+class GPTNGMETokenizer(PreTrainedTokenizer):
+    model_input_names = ["input_ids", "attention_mask"]
+    vocab_file = "vocab.json"
+    vocab_files_names = {"vocab_file": vocab_file}
+
+    def __init__(
+        self, vocab_file, eos_token="\n", pad_token="\n", unk_token="<unk>", **kwargs
+    ):
+        eos_token = (
+            AddedToken(
+                eos_token,
+                lstrip=False,
+                rstrip=False,
+            )
+            if isinstance(eos_token, str)
+            else eos_token
+        )
+        pad_token = (
+            AddedToken(
+                pad_token,
+                lstrip=False,
+                rstrip=False,
+            )
+            if isinstance(pad_token, str)
+            else pad_token
+        )
+        unk_token = (
+            AddedToken(
+                unk_token,
+                lstrip=False,
+                rstrip=False,
+            )
+            if isinstance(unk_token, str)
+            else unk_token
+        )
+
+        super().__init__(
+            eos_token=eos_token, pad_token=pad_token, unk_token=unk_token, **kwargs
+        )
+
+        self._ngram2word2idx = {}
+        self._ngram2idx2word = {}
+        self._current_max_idx = 0
+        self._frequencies: Counter = Counter()
+
+        self._load_from_file(vocab_file)
+
+    def _load_from_file(self, filename: str):
+        """Loads a dictionary from a file."""
+        vocab_file = load_vocab(filename)
+        self.ngram = vocab_file["ngram"]
+
+        if "\n" not in vocab_file["vocab"]:
+            self._add_ngram("\n", 1)
+
+        for token in vocab_file["vocab"]:
+            self._add_ngram(token["token"], token["ngram"])
+            self._frequencies.update({token["token"]: token["frequency"]})
+
+    def _add_ngram(self, word, ngram: int) -> int:
+        """Add a new n-gram token to the dictionary."""
+        self._frequencies.update({word: 1})
+
+        if ngram not in self._ngram2idx2word:
+            self._ngram2idx2word[ngram] = {self._current_max_idx: word}
+            self._ngram2word2idx[ngram] = {word: self._current_max_idx}
+            self._current_max_idx += 1
+        else:
+            if word not in self._ngram2word2idx[ngram]:
+                self._ngram2idx2word[ngram][self._current_max_idx] = word
+                self._ngram2word2idx[ngram][word] = self._current_max_idx
+                self._current_max_idx += 1
+
+        return self._ngram2word2idx[ngram][word]
+
+    def _is_contiguous(self):
+        vocab_size = len(self)
+        return list(range(vocab_size)) == [idx for idx, token in self._get_all_tokens()]
+
+    def _get_all_tokens(self):
+        """Returns all tokens in the dictionary."""
+        for ngram in range(1, self.ngram + 1):
+            for idx, token in self._ngram2idx2word[ngram].items():
+                yield idx, token
+
+    def save_vocabulary(
+        self, save_directory: str, filename_prefix: Optional[str] = None
+    ) -> Tuple[str]:
+        filename = os.path.join(
+            save_directory,
+            (filename_prefix + "-" if filename_prefix else ""),
+            self.vocab_file,
+        )
+
+        index = 0
+        vocab = {"ngram": self.ngram, "vocab": []}
+
+        for ngram in range(1, self.ngram + 1):
+            for idx, token in self._ngram2idx2word[ngram].items():
+                if index != idx:
+                    index = idx
+
+                try:
+                    frequency = self._frequencies[token]
+                except KeyError:
+                    frequency = -1
+
+                index += 1
+                vocab["vocab"].append(
+                    {
+                        "token": token,
+                        "index": idx,
+                        "frequency": frequency,
+                        "ngram": ngram,
+                    }
+                )
+
+        with open(filename, "w", encoding="utf-8") as writer:
+            json.dump(vocab, writer, indent=4, ensure_ascii=False)
+
+        return (filename,)
+
+    @property
+    def vocab_size(self) -> int:
+        return self._current_max_idx
+
+    def retokenize(self, input_ids, *args, **kwargs):
+        decoded = self.convert_ids_to_tokens(input_ids)
+        sequence = "".join(decoded)
+        new_decoded = self(sequence, *args, **kwargs).input_ids
+        return new_decoded
+
+    def _tokenize(self, text):
+        ngram_sequences = []
+        for n in range(1, self.ngram + 1):
+            words = ["<start>" for _ in range(1, n)]
+            words.extend(list(text))
+
+            tokens = []
+            for i, word in enumerate(ngram_tokenizer(words, n)):
+                if "<start>" in word:
+                    word = [w for w in list(word) if w != "<start>"]
+                tokens.append("".join(word))
+
+            ngram_sequences.append(tokens)
+
+        return ngram_sequences
+
+    def get_idx(self, token: str, ngram: Optional[int] = None) -> int:
+        if ngram:
+            if token in self._ngram2word2idx[ngram]:
+                return self._ngram2word2idx[ngram][token]
+            else:
+                return self._ngram2word2idx[1]["<unk>"]
+
+        for ngram in range(1, self.ngram + 1):
+            if token in self._ngram2word2idx[ngram]:
+                return self._ngram2word2idx[ngram][token]
+
+        return self._ngram2word2idx[1]["<unk>"]
+
+    def _convert_ngram_tokens_to_ids(self, ngram_tokens: List[str]) -> List[int]:
+        return [self.get_idx(token) for token in ngram_tokens]
+
+    def convert_tokens_to_ids(self, tokens: List[List[str]]):
+        if not tokens:
+            return []
+
+        if isinstance(tokens, str):
+            return self.get_idx(tokens)
+
+        return [
+            self._convert_ngram_tokens_to_ids(ngram_tokens) for ngram_tokens in tokens
+        ]
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self.get_item_for_index(index)
+
+    def get_item_for_index(self, idx) -> str:
+        """Return the token for a given index."""
+        for idxs in self._ngram2idx2word.values():
+            if idx in idxs:
+                return idxs[idx]
+
+        return self.unk_token
+
+    def _decode(
+        self, token_ids: List[List[int]], skip_special_tokens: bool = False, **kwargs
+    ) -> str:
+        return "".join(self.convert_ids_to_tokens(token_ids[0]))
+
+    def debug_decode(self, token_ids: List[List[int]]):
+        for n in range(1, self.ngram + 1):
+            print(f"{n}-gram: {self.convert_ids_to_tokens(token_ids[n-1])}")
+
+    def _pad(
+        self,
+        encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
+        max_length: Optional[int] = None,
+        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+        pad_to_multiple_of: Optional[int] = None,
+        return_attention_mask: Optional[bool] = None,
+    ) -> dict:
+        """
+        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+        Args:
+            encoded_inputs:
+                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
+            max_length: maximum length of the returned list and optionally padding length (see below).
+                Will truncate by taking into account the special tokens.
+            padding_strategy: PaddingStrategy to use for padding.
+
+                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+                - PaddingStrategy.DO_NOT_PAD: Do not pad
+                The tokenizer padding sides are defined in self.padding_side:
+
+                    - 'left': pads on the left of the sequences
+                    - 'right': pads on the right of the sequences
+            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+                `>= 7.5` (Volta).
+            return_attention_mask:
+                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+        """
+        # encoded_inputs == one sample -> List[List[int]]
+
+        # Load from model defaults
+        if return_attention_mask is None:
+            return_attention_mask = "attention_mask" in self.model_input_names
+
+        required_input = encoded_inputs[self.model_input_names[0]]
+        # PHA: Check if we have a list of list of list, then we unpack
+        if (
+            len(required_input) != 0
+            and isinstance(required_input[0], list)
+            and isinstance(required_input[0][0], list)
+        ):
+            required_input = required_input[0]
+
+        if padding_strategy == PaddingStrategy.LONGEST:
+            max_length = len(required_input)
+
+        if (
+            max_length is not None
+            and pad_to_multiple_of is not None
+            and (max_length % pad_to_multiple_of != 0)
+        ):
+            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+        needs_to_be_padded = (
+            padding_strategy != PaddingStrategy.DO_NOT_PAD
+            and len(required_input[0]) != max_length
+        )
+
+        # Initialize attention mask if not present.
+        if return_attention_mask and "attention_mask" not in encoded_inputs:
+            if len(required_input) == 0:
+                encoded_inputs["attention_mask"] = []
+            else:
+                encoded_inputs["attention_mask"] = [1] * len(required_input[0])
+
+        if needs_to_be_padded:
+            difference = max_length - len(required_input[0])
+
+            if self.padding_side == "right":
+                if return_attention_mask:
+                    encoded_inputs["attention_mask"] = (
+                        encoded_inputs["attention_mask"] + [0] * difference
+                    )
+                if "token_type_ids" in encoded_inputs:
+                    encoded_inputs["token_type_ids"] = (
+                        encoded_inputs["token_type_ids"]
+                        + [self.pad_token_type_id] * difference
+                    )
+                if "special_tokens_mask" in encoded_inputs:
+                    encoded_inputs["special_tokens_mask"] = (
+                        encoded_inputs["special_tokens_mask"] + [1] * difference
+                    )
+                for i in range(len(encoded_inputs[self.model_input_names[0]])):
+                    encoded_inputs[self.model_input_names[0]][i] = (
+                        required_input[i] + [self.pad_token_id] * difference
+                    )
+            elif self.padding_side == "left":
+                if return_attention_mask:
+                    encoded_inputs["attention_mask"] = [
+                        0
+                    ] * difference + encoded_inputs["attention_mask"]
+                if "token_type_ids" in encoded_inputs:
+                    encoded_inputs["token_type_ids"] = [
+                        self.pad_token_type_id
+                    ] * difference + encoded_inputs["token_type_ids"]
+                if "special_tokens_mask" in encoded_inputs:
+                    encoded_inputs["special_tokens_mask"] = [
+                        1
+                    ] * difference + encoded_inputs["special_tokens_mask"]
+
+                for i in range(len(encoded_inputs[self.model_input_names[0]])):
+                    encoded_inputs[self.model_input_names[0]][i] = [
+                        self.pad_token_id
+                    ] * difference + required_input[i]
+            else:
+                raise ValueError("Invalid padding strategy:" + str(self.padding_side))
+
+        return encoded_inputs
+
+    def pad(
+        self,
+        encoded_inputs: Union[
+            BatchEncoding,
+            List[BatchEncoding],
+            Dict[str, EncodedInput],
+            Dict[str, List[EncodedInput]],
+            List[Dict[str, EncodedInput]],
+        ],
+        padding: Union[bool, str, PaddingStrategy] = True,
+        max_length: Optional[int] = None,
+        pad_to_multiple_of: Optional[int] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        verbose: bool = True,
+    ) -> BatchEncoding:
+        """
+        Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
+        in the batch.
+
+        Padding side (left/right) padding token ids are defined at the tokenizer level (with `self.padding_side`,
+
+        `self.pad_token_id` and `self.pad_token_type_id`).
+
+        Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the
+        text followed by a call to the `pad` method to get a padded encoding.
+
+        <Tip>
+
+        If the `encoded_inputs` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
+        result will use the same type unless you provide a different tensor type with `return_tensors`. In the case of
+        PyTorch tensors, you will lose the specific device of your tensors however.
+
+        </Tip>
+
+        Args:
+            encoded_inputs ([`BatchEncoding`], list of [`BatchEncoding`], `Dict[str, List[int]]`, `Dict[str, List[List[int]]` or `List[Dict[str, List[int]]]`):
+                Tokenized inputs. Can represent one input ([`BatchEncoding`] or `Dict[str, List[int]]`) or a batch of
+                tokenized inputs (list of [`BatchEncoding`], *Dict[str, List[List[int]]]* or *List[Dict[str,
+                List[int]]]*) so you can use this method during preprocessing as well as in a PyTorch Dataloader
+                collate function.
+
+                Instead of `List[int]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors), see
+                the note above for the return type.
+            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `True`):
+                 Select a strategy to pad the returned sequences (according to the model's padding side and padding
+                 index) among:
+
+                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
+                  sequence if provided).
+                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
+                  acceptable input length for the model if that argument is not provided.
+                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
+                  lengths).
+            max_length (`int`, *optional*):
+                Maximum length of the returned list and optionally padding length (see above).
+            pad_to_multiple_of (`int`, *optional*):
+                If set will pad the sequence to a multiple of the provided value.
+
+                This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability
+                `>= 7.5` (Volta).
+            return_attention_mask (`bool`, *optional*):
+                Whether to return the attention mask. If left to the default, will return the attention mask according
+                to the specific tokenizer's default, defined by the `return_outputs` attribute.
+
+                [What are attention masks?](../glossary#attention-mask)
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors instead of list of python integers. Acceptable values are:
+
+                - `'tf'`: Return TensorFlow `tf.constant` objects.
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return Numpy `np.ndarray` objects.
+            verbose (`bool`, *optional*, defaults to `True`):
+                Whether or not to print more information and warnings.
+        """
+
+        # Problem: The pad function checks if the encoded_inputs is a list or not
+        # If it is a list it assumes that we have batches
+        # With ngme encoding the input is always a list
+
+        # If we have a list of dicts, let's convert it in a dict of lists
+        # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
+        if isinstance(encoded_inputs, (list, tuple)) and isinstance(
+            encoded_inputs[0], Mapping
+        ):
+            encoded_inputs = {
+                key: [example[key] for example in encoded_inputs]
+                for key in encoded_inputs[0].keys()
+            }
+
+        # The model's main input name, usually `input_ids`, has be passed for padding
+        if self.model_input_names[0] not in encoded_inputs:
+            raise ValueError(
+                "You should supply an encoding or a list of encodings to this method "
+                f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}"
+            )
+
+        required_input = encoded_inputs[self.model_input_names[0]]
+
+        if required_input is None or (
+            isinstance(required_input, Sized) and len(required_input) == 0
+        ):
+            if return_attention_mask:
+                encoded_inputs["attention_mask"] = []
+            return encoded_inputs
+
+        # If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects
+        # and rebuild them afterwards if no return_tensors is specified
+        # Note that we lose the specific device the tensor may be on for PyTorch
+
+        first_element = required_input[0]
+        # PHA: First element in ngme is a list of list
+        if isinstance(first_element, (list, tuple)):
+            # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
+            for item in required_input:
+                if len(item) != 0:
+                    first_element = item[0]
+                    break
+        # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
+        if not isinstance(first_element, (int, list, tuple)):
+            if is_tf_tensor(first_element):
+                return_tensors = "tf" if return_tensors is None else return_tensors
+            elif is_torch_tensor(first_element):
+                return_tensors = "pt" if return_tensors is None else return_tensors
+            elif isinstance(first_element, np.ndarray):
+                return_tensors = "np" if return_tensors is None else return_tensors
+            else:
+                raise ValueError(
+                    f"type of {first_element} unknown: {type(first_element)}. "
+                    "Should be one of a python, numpy, pytorch or tensorflow object."
+                )
+
+            for key, value in encoded_inputs.items():
+                encoded_inputs[key] = to_py_obj(value)
+
+        # Convert padding_strategy in PaddingStrategy
+        padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
+            padding=padding, max_length=max_length, verbose=verbose
+        )
+
+        required_input = encoded_inputs[self.model_input_names[0]]
+
+        if required_input:
+            if isinstance(required_input[0], (list, tuple)):
+                if len(required_input[0]) > 0 and not isinstance(
+                    required_input[0][0], (list, tuple)
+                ):
+                    encoded_inputs = self._pad(
+                        encoded_inputs,
+                        max_length=max_length,
+                        padding_strategy=padding_strategy,
+                        pad_to_multiple_of=pad_to_multiple_of,
+                        return_attention_mask=return_attention_mask,
+                    )
+                    return BatchEncoding(encoded_inputs, tensor_type=return_tensors)
+
+        batch_size = len(required_input)
+        assert all(
+            len(v) == batch_size for v in encoded_inputs.values()
+        ), "Some items in the output dictionary have a different batch size than others."
+
+        if padding_strategy == PaddingStrategy.LONGEST:
+            max_length = max(len(inputs[0]) for inputs in required_input)
+            padding_strategy = PaddingStrategy.MAX_LENGTH
+
+        batch_outputs = {}
+        for i in range(batch_size):
+            inputs = dict((k, v[i]) for k, v in encoded_inputs.items())
+            outputs = self._pad(
+                inputs,
+                max_length=max_length,
+                padding_strategy=padding_strategy,
+                pad_to_multiple_of=pad_to_multiple_of,
+                return_attention_mask=return_attention_mask,
+            )
+
+            for key, value in outputs.items():
+                if key not in batch_outputs:
+                    batch_outputs[key] = []
+                batch_outputs[key].append(value)
+
+        return BatchEncoding(batch_outputs, tensor_type=return_tensors)
+
+    def prepare_for_model(
+        self,
+        ids: List[int],
+        pair_ids: Optional[List[int]] = None,
+        add_special_tokens: bool = True,
+        padding: Union[bool, str, PaddingStrategy] = False,
+        truncation: Union[bool, str, TruncationStrategy] = None,
+        max_length: Optional[int] = None,
+        stride: int = 0,
+        pad_to_multiple_of: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        return_token_type_ids: Optional[bool] = None,
+        return_attention_mask: Optional[bool] = None,
+        return_overflowing_tokens: bool = False,
+        return_special_tokens_mask: bool = False,
+        return_offsets_mapping: bool = False,
+        return_length: bool = False,
+        verbose: bool = True,
+        prepend_batch_axis: bool = False,
+        **kwargs,
+    ) -> BatchEncoding:
+        """
+        Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
+        adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
+        manages a moving window (with user defined stride) for overflowing tokens. Please Note, for *pair_ids*
+        different than `None` and *truncation_strategy = longest_first* or `True`, it is not possible to return
+        overflowing tokens. Such a combination of arguments will raise an error.
+        Args:
+            ids (`List[int]`):
+                Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
+                `convert_tokens_to_ids` methods.
+            pair_ids (`List[int]`, *optional*):
+                Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
+                and `convert_tokens_to_ids` methods.
+        """
+
+        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
+        (
+            padding_strategy,
+            truncation_strategy,
+            max_length,
+            kwargs,
+        ) = self._get_padding_truncation_strategies(
+            padding=padding,
+            truncation=truncation,
+            max_length=max_length,
+            pad_to_multiple_of=pad_to_multiple_of,
+            verbose=verbose,
+            **kwargs,
+        )
+
+        pair = bool(pair_ids is not None)
+
+        if len(ids) == 0:
+            len_ids = 0
+        else:
+            len_ids = len(ids[0])
+
+        if pair and len(pair_ids) == 0:
+            len_pair_ids = 0
+        elif pair and len(pair_ids) > 0:
+            len_pair_ids = len(pair_ids[0])
+        else:
+            len_pair_ids = 0
+
+        if return_token_type_ids and not add_special_tokens:
+            raise ValueError(
+                "Asking to return token_type_ids while setting add_special_tokens to False "
+                "results in an undefined behavior. Please set add_special_tokens to True or "
+                "set return_token_type_ids to None."
+            )
+
+        if (
+            return_overflowing_tokens
+            and truncation_strategy == TruncationStrategy.LONGEST_FIRST
+            and pair_ids is not None
+        ):
+            raise ValueError(
+                "Not possible to return overflowing tokens for pair of sequences with the "
+                "`longest_first`. Please select another truncation strategy than `longest_first`, "
+                "for instance `only_second` or `only_first`."
+            )
+
+        # Load from model defaults
+        if return_token_type_ids is None:
+            return_token_type_ids = "token_type_ids" in self.model_input_names
+        if return_attention_mask is None:
+            return_attention_mask = "attention_mask" in self.model_input_names
+
+        encoded_inputs = {}
+
+        # Compute the total size of the returned encodings
+        total_len = (
+            len_ids
+            + len_pair_ids
+            + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
+        )
+
+        # Truncation: Handle max sequence length
+        overflowing_tokens = []
+        if (
+            truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE
+            and max_length
+            and total_len > max_length
+        ):
+            ids, pair_ids, overflowing_tokens = self.truncate_sequences(
+                ids,
+                pair_ids=pair_ids,
+                num_tokens_to_remove=total_len - max_length,
+                truncation_strategy=truncation_strategy,
+                stride=stride,
+            )
+
+        if return_overflowing_tokens:
+            encoded_inputs["overflowing_tokens"] = overflowing_tokens
+            encoded_inputs["num_truncated_tokens"] = total_len - max_length
+
+        # Add special tokens
+        if add_special_tokens:
+            sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
+            token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
+        else:
+            sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
+            token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
+
+        # Build output dictionary
+        encoded_inputs["input_ids"] = sequence
+        if return_token_type_ids:
+            encoded_inputs["token_type_ids"] = token_type_ids
+        if return_special_tokens_mask:
+            if add_special_tokens:
+                encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(
+                    ids, pair_ids
+                )
+            else:
+                encoded_inputs["special_tokens_mask"] = [0] * len(sequence)
+
+        # Check lengths
+        self._eventual_warn_about_too_long_sequence(
+            encoded_inputs["input_ids"], max_length, verbose
+        )
+
+        # Padding
+        if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
+            encoded_inputs = self.pad(
+                encoded_inputs,
+                max_length=max_length,
+                padding=padding_strategy.value,
+                pad_to_multiple_of=pad_to_multiple_of,
+                return_attention_mask=return_attention_mask,
+            )
+
+        if return_length:
+            encoded_inputs["length"] = len(encoded_inputs["input_ids"])
+
+        batch_outputs = BatchEncoding(
+            encoded_inputs,
+            tensor_type=return_tensors,
+            prepend_batch_axis=prepend_batch_axis,
+        )
+
+        return batch_outputs
+
+    def build_inputs_with_special_tokens(
+        self,
+        token_ids_0: List[List[int]],
+        token_ids_1: Optional[List[List[int]]] = None,
+    ) -> List[List[int]]:
+        """
+        Concatenate nested ngram sequences.
+
+        Args:
+            token_ids_0 (`List[List[int]]`): The first tokenized sequence.
+            token_ids_1 (`List[List[int]]`, *optional*): The second tokenized sequence.
+
+        Returns:
+            `List[List[int]]`: The model input with special tokens.
+        """
+        if token_ids_1 is None or len(token_ids_1) == 0:
+            return token_ids_0
+
+        if len(token_ids_0) == 0:
+            return token_ids_1
+
+        return np.concatenate(
+            (np.array(token_ids_0), np.array(token_ids_1)), axis=1
+        ).tolist()
+
+    def truncate_sequences(
+        self,
+        ids: List[int],
+        pair_ids: Optional[List[int]] = None,
+        num_tokens_to_remove: int = 0,
+        truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
+        stride: int = 0,
+    ) -> Tuple[List[int], List[int], List[int]]:
+        """
+        Truncates a sequence pair in-place following the strategy.
+        Args:
+            ids (`List[int]`):
+                Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
+                `convert_tokens_to_ids` methods.
+            pair_ids (`List[int]`, *optional*):
+                Tokenized input ids of the second sequence. Can be obtained from a string by chaining the `tokenize`
+                and `convert_tokens_to_ids` methods.
+            num_tokens_to_remove (`int`, *optional*, defaults to 0):
+                Number of tokens to remove using the truncation strategy.
+            truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
+                The strategy to follow for truncation. Can be:
+                - `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+                  maximum acceptable input length for the model if that argument is not provided. This will truncate
+                  token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a
+                  batch of pairs) is provided.
+                - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the
+                  maximum acceptable input length for the model if that argument is not provided. This will only
+                  truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+                - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the
+                  maximum acceptable input length for the model if that argument is not provided. This will only
+                  truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
+                - `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
+                  than the model maximum admissible input size).
+            stride (`int`, *optional*, defaults to 0):
+                If set to a positive number, the overflowing tokens returned will contain some tokens from the main
+                sequence returned. The value of this argument defines the number of additional tokens.
+        Returns:
+            `Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
+            overflowing tokens. Note: The *longest_first* strategy returns empty list of overflowing tokens if a pair
+            of sequences (or a batch of pairs) is provided.
+        """
+        if num_tokens_to_remove <= 0:
+            return ids, pair_ids, []
+
+        if not isinstance(truncation_strategy, TruncationStrategy):
+            truncation_strategy = TruncationStrategy(truncation_strategy)
+
+        overflowing_tokens = []
+        if truncation_strategy == TruncationStrategy.ONLY_FIRST or (
+            truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None
+        ):
+            ids = np.array(ids)
+
+            # PHA: I think we only truncate with longest first
+            if ids.shape[1] > num_tokens_to_remove:
+                window_len = min(ids.shape[1], stride + num_tokens_to_remove)
+                if self.truncation_side == "left":
+                    overflowing_tokens = ids[:, :window_len]
+                    ids = ids[:, num_tokens_to_remove:]
+                elif self.truncation_side == "right":
+                    overflowing_tokens = ids[-window_len:]
+                    ids = ids[:, :-num_tokens_to_remove]
+                else:
+                    raise ValueError(
+                        f"invalid truncation strategy: {self.truncation_side}, use 'left' or 'right'."
+                    )
+
+                ids = ids.tolist()
+
+            else:
+                error_msg = (
+                    f"We need to remove {num_tokens_to_remove} to truncate the input "
+                    f"but the first sequence has a length {len(ids)}. "
+                )
+                if truncation_strategy == TruncationStrategy.ONLY_FIRST:
+                    error_msg = (
+                        error_msg + "Please select another truncation strategy than "
+                        f"{truncation_strategy}, for instance 'longest_first' or 'only_second'."
+                    )
+                logger.error(error_msg)
+        elif truncation_strategy == TruncationStrategy.LONGEST_FIRST:
+            logger.warning(
+                "Be aware, overflowing tokens are not returned for the setting you have chosen,"
+                f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' "
+                "truncation strategy. So the returned list will always be empty even if some "
+                "tokens have been removed."
+            )
+            ids = np.array(ids)
+            pair_ids = np.array(pair_ids)
+
+            for _ in range(num_tokens_to_remove):
+                if pair_ids is None or ids.shape[1] > pair_ids.shape[1]:
+                    if self.truncation_side == "right":
+                        ids = ids[:, :-1]
+                    elif self.truncation_side == "left":
+                        ids = ids[:, 1:]
+                    else:
+                        raise ValueError(
+                            "invalid truncation strategy:" + str(self.truncation_side)
+                        )
+                else:
+                    if self.truncation_side == "right":
+                        pair_ids = pair_ids[:, :-1]
+                    elif self.truncation_side == "left":
+                        pair_ids = pair_ids[:, 1:]
+                    else:
+                        raise ValueError(
+                            "invalid truncation strategy:" + str(self.truncation_side)
+                        )
+
+            ids = ids.tolist()
+            pair_ids = pair_ids.tolist()
+
+        elif (
+            truncation_strategy == TruncationStrategy.ONLY_SECOND
+            and pair_ids is not None
+        ):
+            raise NotImplementedError(
+                "PHA: I think we only truncate with longest first"
+            )
+            if len(pair_ids) > num_tokens_to_remove:
+                window_len = min(len(pair_ids), stride + num_tokens_to_remove)
+                if self.truncation_side == "right":
+                    overflowing_tokens = pair_ids[-window_len:]
+                    pair_ids = pair_ids[:-num_tokens_to_remove]
+                elif self.truncation_side == "left":
+                    overflowing_tokens = pair_ids[:window_len]
+                    pair_ids = pair_ids[num_tokens_to_remove:]
+                else:
+                    raise ValueError(
+                        "invalid truncation strategy:" + str(self.truncation_side)
+                    )
+            else:
+                logger.error(
+                    f"We need to remove {num_tokens_to_remove} to truncate the input "
+                    f"but the second sequence has a length {len(pair_ids)}. "
+                    f"Please select another truncation strategy than {truncation_strategy}, "
+                    "for instance 'longest_first' or 'only_first'."
+                )
+
+        return (ids, pair_ids, overflowing_tokens)
+
+    def _token_to_n_order(self, token: str) -> int:
+        """Get N-gram order for a token"""
+        for n_gram, word2idx in self._ngram2word2idx.items():
+            if token in word2idx:
+                return n_gram
+
+        return 0
+
+    def create_weight_tensor(self) -> torch.Tensor:
+        unked_freqs = self._frequencies.most_common()
+
+        t = torch.ones(len(self))
+
+        for token, freq in unked_freqs:
+            t[self._ngram2word2idx[self._token_to_n_order(token)][token]] = freq
+
+        # Ensure the only whitespace character is weighted
+        t[self._ngram2word2idx[1][" "]] = 1.0
+
+        normed_weights = torch.tensor([(1 - (x / (max(t) + 1))).item() for x in t])
+
+        marker_tokens = [self.get_idx("<unk>", n) for n in range(1, self.ngram + 1)]
+        marker_tokens.extend(
+            [self.get_idx("<start>", n) for n in range(1, self.ngram + 1)]
+        )
+        # Instead of explicit ignore indexes, we use the weight vector and set target idxs to 0
+        for marker in marker_tokens:
+            normed_weights[marker] = 0
+
+        return normed_weights
+
+
+class TestTokenizer(unittest.TestCase):
+    def test_one(self):
+        vocab_file = "/home/phmaker/Projects/ngme/vocabs/1-gram-babylm.json"
+
+        t = NGMETokenizer(vocab_file)
+        self.assertEqual(t.get_idx("<unk>", 1), 1)
+
+        result = t("hello world")
+        self.assertEqual(result.input_ids, [16, 3, 11, 11, 8, 2, 21, 8, 9, 11, 12])
+
+        result = t("<unk>")
+        self.assertEqual(result.input_ids, [1, 13, 5, 24, 1])
+
+        result = t(["hello world", "<unk>"])
+        self.assertEqual(
+            result.input_ids,
+            [[16, 3, 11, 11, 8, 2, 21, 8, 9, 11, 12], [1, 13, 5, 24, 1]],
+        )
+
+    def test_three(self):
+        vocab_file = "/home/phmaker/Projects/ngme/vocabs/3-gram-babylm.json"
+
+        t = NGMETokenizer(vocab_file)
+
+        result = t("hello world")
+        self.assertEqual(result.input_ids, [16, 3, 11, 11, 8, 2, 21, 8, 9, 11, 12])
+
+        result = t("hello", return_ngram_sequences=True)
+
+        result = t(["hello world"], return_ngram_sequences=True)
+        two_gram_expected = [[16, 208, 229, 230, 231, 1, 1, 312, 257, 499, 306]]
+
+        self.assertEqual(result["gram_2_sequence"], two_gram_expected)
+        self.assertEqual(t._ngram2idx2word[1][16], "h")
+        self.assertEqual(t._ngram2idx2word[2][208], "he")
+        self.assertEqual(t._ngram2idx2word[2][229], "el")
+
+    def test_unks(self):
+        vocab_file = "/home/phmaker/Projects/ngme/vocabs/2-gram-wiki-en.json"
+        t = NGMETokenizer(vocab_file)
+        result = t("OciVDjöShG", return_ngram_sequences=True, return_tensors="pt")
+
+    def test_decode(self):
+        vocab_file = "/home/phmaker/Projects/ngme/vocabs/3-gram-babylm.json"
+        t = NGMETokenizer(vocab_file)
+        decoded = t.decode(208)
+        assert decoded == "he"
+
+    def test_padding(self):
+        vocab_file = "/home/phmaker/Projects/ngme/vocabs/3-gram-babylm.json"
+        t = NGMETokenizer(vocab_file)
+        result = t(
+            "hello world",
+            return_tensors="pt",
+            padding="max_length",
+            max_length=20,
+            return_ngram_sequences=True,
+        )
+
+        self.assertEqual(result.input_ids.shape, (1, 20))
+        self.assertEqual(result.gram_2_sequence.shape, (1, 20))
+        self.assertEqual(result.gram_3_sequence.shape, (1, 20))
+
+    def test_truncation(self):
+        vocab_file = "/home/phmaker/Projects/ngme/vocabs/3-gram-babylm.json"
+        t = NGMETokenizer(vocab_file)
+
+        result = t(
+            "hello world",
+            return_tensors="pt",
+            truncation=True,
+            max_length=5,
+            return_ngram_sequences=True,
+        )
+        self.assertEqual(result.input_ids.shape, (1, 5))
+        self.assertEqual(result.gram_2_sequence.shape, (1, 5))
+
+    def test_padding_and_truncation(self):
+        vocab_file = "/home/phmaker/Projects/ngme/vocabs/3-gram-babylm.json"
+        t = NGMETokenizer(vocab_file)
+
+        result = t(
+            ["four", "something longer"],
+            return_tensors="pt",
+            padding="max_length",
+            truncation=True,
+            max_length=5,
+            return_ngram_sequences=True,
+        )
+        self.assertEqual(result.input_ids.shape, (2, 5))
+        self.assertEqual(result.gram_2_sequence.shape, (2, 5))
+
+
+if __name__ == "__main__":
+    unittest.main()