Delete modeling_hf_nomic_bert.py

modeling_hf_nomic_bert.py

@@ -1,1227 +0,0 @@
-# Copyright (c) 2022, Tri Dao.
-# This BERT implementation is based on our MLPerf 2.0 and MLPerf 2.1 BERT implementation.
-# https://github.com/mlcommons/training_results_v2.0/blob/main/HazyResearch/benchmarks/bert/implementations/pytorch/modeling.py
-# https://github.com/mlcommons/training_results_v2.1/blob/main/Azure-HazyResearch/benchmarks/bert/implementations/ND96amsr_A100_v4/modeling.py
-
-import logging
-
-# Inspired by https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py
-import os
-import re
-from collections import OrderedDict
-from functools import partial
-from typing import List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange, repeat
-from safetensors.torch import load_file as safe_load_file
-from transformers import GPT2Config, PreTrainedModel
-from transformers.models.bert.modeling_bert import (
-    BaseModelOutputWithPoolingAndCrossAttentions,
-    MaskedLMOutput,
-    SequenceClassifierOutput,
-)
-from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, WEIGHTS_INDEX_NAME, WEIGHTS_NAME
-from transformers.utils.hub import cached_file, get_checkpoint_shard_files
-
-from .configuration_hf_nomic_bert import NomicBertConfig
-
-logger = logging.getLogger(__name__)
-
-
-# adapted from flash attention, added safe serialization option for hf models
-def state_dict_from_pretrained(model_name, safe_serialization=False, device=None, dtype=None):
-    # If not fp32, then we don't want to load directly to the GPU
-    mapped_device = "cpu" if dtype not in [torch.float32, None] else device
-    is_sharded = False
-    load_safe = False
-    resolved_archive_file = None
-
-    weights_path = os.path.join(model_name, WEIGHTS_NAME)
-    weights_index_path = os.path.join(model_name, WEIGHTS_INDEX_NAME)
-    safe_weights_path = os.path.join(model_name, SAFE_WEIGHTS_NAME)
-    safe_weights_index_path = os.path.join(model_name, SAFE_WEIGHTS_INDEX_NAME)
-
-    if os.path.isfile(weights_path):
-        resolved_archive_file = cached_file(model_name, WEIGHTS_NAME, _raise_exceptions_for_missing_entries=False)
-    elif os.path.isfile(weights_index_path):
-        resolved_archive_file = cached_file(model_name, WEIGHTS_INDEX_NAME, _raise_exceptions_for_missing_entries=False)
-        is_sharded = True
-    elif os.path.isfile(safe_weights_path):
-        resolved_archive_file = cached_file(model_name, SAFE_WEIGHTS_NAME, _raise_exceptions_for_missing_entries=False)
-        load_safe = True
-    elif os.path.isfile(safe_weights_index_path):
-        resolved_archive_file = cached_file(
-            model_name, SAFE_WEIGHTS_INDEX_NAME, _raise_exceptions_for_missing_entries=False
-        )
-        is_sharded = True
-        load_safe = True
-    else:  # Try loading from HF hub instead of from local files
-        weight_name = WEIGHTS_NAME if not safe_serialization else SAFE_WEIGHTS_NAME
-        resolved_archive_file = cached_file(model_name, weight_name, _raise_exceptions_for_missing_entries=False)
-        if resolved_archive_file is None:
-            weight_index = WEIGHTS_INDEX_NAME if not safe_serialization else SAFE_WEIGHTS_INDEX_NAME
-            resolved_archive_file = cached_file(model_name, weight_index, _raise_exceptions_for_missing_entries=False)
-            if resolved_archive_file is not None:
-                is_sharded = True
-
-        load_safe = safe_serialization
-
-    if resolved_archive_file is None:
-        raise EnvironmentError(f"Model name {model_name} was not found.")
-
-    if load_safe:
-        loader = partial(safe_load_file, device=mapped_device)
-    else:
-        loader = partial(torch.load, map_location=mapped_device)
-
-    if is_sharded:
-        # resolved_archive_file becomes a list of files that point to the different
-        # checkpoint shards in this case.
-        resolved_archive_file, sharded_metadata = get_checkpoint_shard_files(model_name, resolved_archive_file)
-        state_dict = {}
-        for sharded_file in resolved_archive_file:
-            state_dict.update(loader(sharded_file))
-    else:
-        state_dict = loader(resolved_archive_file)
-    # Convert dtype before moving to GPU to save memory
-    if dtype is not None:
-        state_dict = {k: v.to(dtype=dtype) for k, v in state_dict.items()}
-    state_dict = {k: v.to(device=device) for k, v in state_dict.items()}
-    return state_dict
-
-
-def filter_shapes(state_dict, model):
-    """
-    Filters the state dict to match the current model shape.
-    """
-    filtered_state_dict = {}
-    for key, value in state_dict.items():
-        if key in model.state_dict():
-            if value.shape == model.state_dict()[key].shape:
-                filtered_state_dict[key] = value
-    return filtered_state_dict
-
-
-def remap_bert_state_dict(
-    state_dict,
-    config,
-    remove_bert=False,
-    remove_cls_weights=False,
-    add_pooling_layer=False,
-):
-    """
-    Map the state_dict of a Huggingface BERT model to be flash_attn compatible.
-    """
-
-    def add_bert_prefix(key):
-        # prepend bert. to the key
-        if key.startswith("bert.") or key.startswith("cls."):
-            return key
-        return f"bert.{key}"
-
-    state_dict = OrderedDict((add_bert_prefix(k), v) for k, v in state_dict.items())
-
-    # LayerNorm
-    def key_mapping_ln_gamma_beta(key):
-        key = re.sub(r"LayerNorm.gamma$", "LayerNorm.weight", key)
-        key = re.sub(r"LayerNorm.beta$", "LayerNorm.bias", key)
-        return key
-
-    state_dict = OrderedDict((key_mapping_ln_gamma_beta(k), v) for k, v in state_dict.items())
-
-    # Layers
-    def key_mapping_layers(key):
-        return re.sub(r"^bert.encoder.layer\.", "bert.encoder.layers.", key)
-
-    state_dict = OrderedDict((key_mapping_layers(k), v) for k, v in state_dict.items())
-
-    # LayerNorm
-    def key_mapping_ln(key):
-        key = re.sub(r"^bert.embeddings.LayerNorm.", "bert.emb_ln.", key)
-        key = re.sub(
-            r"^bert.encoder.layers.(\d+).attention.output.LayerNorm.(weight|bias)",
-            r"bert.encoder.layers.\1.norm1.\2",
-            key,
-        )
-        key = re.sub(
-            r"^bert.encoder.layers.(\d+).output.LayerNorm.(weight|bias)",
-            r"bert.encoder.layers.\1.norm2.\2",
-            key,
-        )
-        key = re.sub(
-            r"^cls.predictions.transform.LayerNorm.(weight|bias)",
-            r"cls.predictions.transform.layer_norm.\1",
-            key,
-        )
-        return key
-
-    state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())
-
-    # MLP
-    def key_mapping_mlp(key):
-        key = re.sub(
-            r"^bert.encoder.layers.(\d+).intermediate.dense.(weight|bias)",
-            r"bert.encoder.layers.\1.mlp.fc1.\2",
-            key,
-        )
-        key = re.sub(
-            r"^bert.encoder.layers.(\d+).output.dense.(weight|bias)",
-            r"bert.encoder.layers.\1.mlp.fc2.\2",
-            key,
-        )
-        return key
-
-    state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())
-
-    # Attention
-    last_layer_subset = getattr(config, "last_layer_subset", False)
-    for d in range(config.num_hidden_layers):
-        if f"bert.encoder.layers.{d}.attention.self.query.weight" not in state_dict:
-            continue
-        Wq = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.query.weight")
-        Wk = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.key.weight")
-        Wv = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.value.weight")
-        bq = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.query.bias")
-        bk = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.key.bias")
-        bv = state_dict.pop(f"bert.encoder.layers.{d}.attention.self.value.bias")
-        if not (last_layer_subset and d == config.num_hidden_layers - 1):
-            state_dict[f"bert.encoder.layers.{d}.attn.Wqkv.weight"] = torch.cat([Wq, Wk, Wv], dim=0)
-            state_dict[f"bert.encoder.layers.{d}.attn.Wqkv.bias"] = torch.cat([bq, bk, bv], dim=0)
-        else:
-            state_dict[f"bert.encoder.layers.{d}.attn.Wq.weight"] = Wq
-            state_dict[f"bert.encoder.layers.{d}.attn.Wkv.weight"] = torch.cat([Wk, Wv], dim=0)
-            state_dict[f"bert.encoder.layers.{d}.attn.Wq.bias"] = bq
-            state_dict[f"bert.encoder.layers.{d}.attn.Wkv.bias"] = torch.cat([bk, bv], dim=0)
-
-    def key_mapping_attn(key):
-        return re.sub(
-            r"^bert.encoder.layers.(\d+).attention.output.dense.(weight|bias)",
-            r"bert.encoder.layers.\1.attn.out_proj.\2",
-            key,
-        )
-
-    state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
-
-    def key_mapping_decoder_bias(key):
-        return re.sub(r"^cls.predictions.bias", "cls.predictions.decoder.bias", key)
-
-    # remove nsp weights, we don't use
-    state_dict.pop("cls.seq_relationship.weight", None)
-    state_dict.pop("cls.seq_relationship.bias", None)
-    state_dict.pop("bert.embeddings.position_ids", None)
-
-    state_dict = OrderedDict((key_mapping_decoder_bias(k), v) for k, v in state_dict.items())
-
-    if remove_cls_weights:
-        cls_weights = [
-            "cls.predictions.decoder.bias",
-            "cls.predictions.transform.dense.weight",
-            "cls.predictions.transform.dense.bias",
-            "cls.predictions.transform.layer_norm.weight",
-            "cls.predictions.transform.layer_norm.bias",
-            "cls.predictions.decoder.weight",
-        ]
-        for weight in cls_weights:
-            state_dict.pop(weight, None)
-
-    # Word embedding
-    pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
-    if pad_vocab_size_multiple > 1:
-        word_embeddings = state_dict["bert.embeddings.word_embeddings.weight"]
-        state_dict["bert.embeddings.word_embeddings.weight"] = F.pad(
-            word_embeddings, (0, 0, 0, config.vocab_size - word_embeddings.shape[0])
-        )
-        if not remove_cls_weights:
-            decoder_weight = state_dict["cls.predictions.decoder.weight"]
-            state_dict["cls.predictions.decoder.weight"] = F.pad(
-                decoder_weight, (0, 0, 0, config.vocab_size - decoder_weight.shape[0])
-            )
-            # If the vocab was padded, we want to set the decoder bias for those padded indices to be
-            # strongly negative (i.e. the decoder shouldn't predict those indices).
-            # TD [2022-05-09]: I don't think it affects the MLPerf training.
-            if "cls.predictions.decoder.bias" in state_dict:
-                decoder_bias = state_dict["cls.predictions.decoder.bias"]
-                state_dict["cls.predictions.decoder.bias"] = F.pad(
-                    decoder_bias, (0, config.vocab_size - decoder_bias.shape[0]), value=-100.0
-                )
-
-    if add_pooling_layer is False:
-        pooler_weights = [
-            "bert.pooler.dense.weight",
-            "bert.pooler.dense.bias",
-        ]
-        for key in pooler_weights:
-            state_dict.pop(key, None)
-
-    if remove_bert:
-
-        def remove_bert_prefix(key):
-            key = re.sub(r"^bert.", "", key)
-            return key
-
-        state_dict = OrderedDict((remove_bert_prefix(k), v) for k, v in state_dict.items())
-
-    return state_dict
-
-
-class NomicBertPreTrainedModel(PreTrainedModel):
-    """An abstract class to handle weights initialization and
-    a simple interface for dowloading and loading pretrained models.
-    """
-
-    config_class = NomicBertConfig
-    base_model_prefix = "model"
-    supports_gradient_checkpointing = True
-    _no_split_modules = ["Block"]
-    _skip_keys_device_placement = "past_key_values"
-
-    def __init__(self, config, *inputs, **kwargs):
-        super().__init__(config)
-        if not isinstance(config, GPT2Config):
-            raise ValueError(
-                "Parameter config in `{}(config)` should be an instance of class `GPT2Config`. "
-                "To create a model from a Google pretrained model use "
-                "`model = {}.from_pretrained(PRETRAINED_MODEL_NAME)`".format(
-                    self.__class__.__name__, self.__class__.__name__
-                )
-            )
-        self.config = config
-
-    @classmethod
-    def from_pretrained(cls, model_name, config=None, *inputs, **kwargs):
-        """
-        Instantiate a NomicBertPreTrainedModel from a pre-trained model file or a pytorch state dict.
-        Download and cache the pre-trained model file if needed.
-
-        Params:
-            pretrained_model_name_or_path: either:
-                - a path or url to a pretrained model archive containing:
-                    . `bert_config.json` a configuration file for the model
-                    . `pytorch_model.bin` a PyTorch dump of a NomicBertForPretraining instance
-                - a path or url to a pretrained model archive containing:
-                    . `bert_config.json` a configuration file for the model
-                    . `model.chkpt` a TensorFlow checkpoint
-            *inputs, **kwargs: additional input for the specific NomicBert class
-                (ex: num_labels for NomicBertForSequenceClassification)
-        """
-        # Instantiate model.
-        if config is None:
-            config = cls.config_class.from_pretrained(model_name)
-        remove_cls = cls != NomicBertForPreTraining
-        remove_bert_prefix = cls != NomicBertForPreTraining and cls != NomicBertForSequenceClassification
-        ignore_mismatched_shapes = kwargs.pop("ignore_mismatched_sizes", False)
-        num_labels = kwargs.pop("num_labels", None)
-        rotary_scaling_factor = kwargs.pop("rotary_scaling_factor", None)
-        strict = kwargs.pop("strict", True)
-        config.rotary_scaling_factor = rotary_scaling_factor
-        if config.n_positions <= 0 and config.rotary_emb_fraction > 0:
-            config.n_positions = 2048
-        if num_labels:
-            config.num_labels = num_labels
-
-        if "add_pooling_layer" in kwargs:
-            model = cls(config, *inputs, add_pooling_layer=kwargs.pop("add_pooling_layer"))
-        else:
-            model = cls(config, *inputs)
-        # TODO: fix this
-        # Assuming we know what we're doing when loading from disk
-        # Prob a bad assumption but i'm tired and want to train this asap
-        if os.path.exists(model_name):
-            model_path = f"{model_name}/pytorch_model.bin"
-            if os.path.exists(model_path):
-                state_dict = torch.load(f"{model_name}/pytorch_model.bin")
-            else:
-                model_path = f"{model_name}/model.safetensors"
-                if not os.path.exists(model_path):
-                    raise ValueError(f"Model path {model_path} not found")
-                state_dict = safe_load_file(model_path)
-
-            if ignore_mismatched_shapes:
-                state_dict = filter_shapes(state_dict, model)
-            load_return = model.load_state_dict(state_dict, strict=False)
-        else:
-            # TODO: can probably check config class and see if we need to remap from a bert model
-            state_dict = state_dict_from_pretrained(model_name)
-            state_dict = remap_bert_state_dict(
-                state_dict,
-                config,
-                remove_bert=remove_bert_prefix,
-                remove_cls_weights=remove_cls,
-                add_pooling_layer=getattr(config, "add_pooling_layer", False),
-            )
-            if ignore_mismatched_shapes:
-                state_dict = filter_shapes(state_dict, model)
-
-            load_return = model.load_state_dict(state_dict, strict=strict)
-        logger.warning(load_return)
-        return model
-
-    def _set_gradient_checkpointing(self, module, value=False):
-        if isinstance(module, NomicBertEncoder):
-            module.gradient_checkpointing = value
-
-
-# https://github.com/huggingface/transformers/blob/7032e0203262ebb2ebf55da8d2e01f873973e835/src/transformers/models/bert/modeling_bert.py#L748
-def _init_weights(module, initializer_range=0.02):
-    if isinstance(module, nn.Linear):
-        nn.init.normal_(module.weight, std=initializer_range)
-        if module.bias is not None:
-            nn.init.zeros_(module.bias)
-    elif isinstance(module, nn.Embedding):
-        nn.init.normal_(module.weight, std=initializer_range)
-        if module.padding_idx is not None:
-            nn.init.zeros_(module.weight[module.padding_idx])
-
-
-class NomicBertEmbeddings(nn.Module):
-    def __init__(self, config):
-        """
-        If max_position_embeddings <= 0, there's no position embeddings
-        If type_vocab_size <= 0, there's no token type embeddings
-        """
-        super().__init__()
-        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
-        self.max_position_embeddings = config.max_position_embeddings if config.rotary_emb_fraction <= 0 else 0
-        self.type_vocab_size = config.type_vocab_size
-        if self.max_position_embeddings > 0 and config.rotary_emb_fraction <= 0:
-            self.position_embeddings = nn.Embedding(
-                config.max_position_embeddings,
-                config.hidden_size,
-            )
-        if self.type_vocab_size > 0:
-            self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
-
-    def forward(self, input_ids, position_ids=None, token_type_ids=None):
-        """
-        input_ids: (batch, seqlen)
-        position_ids: (batch, seqlen)
-        token_type_ids: (batch, seqlen)
-        """
-        batch_size, seqlen = input_ids.shape
-        embeddings = self.word_embeddings(input_ids)
-
-        if self.type_vocab_size > 0:
-            if token_type_ids is None:
-                token_type_ids = torch.zeros(seqlen, dtype=torch.long, device=input_ids.device)
-            token_type_embeddings = self.token_type_embeddings(token_type_ids)
-            embeddings = embeddings + token_type_embeddings
-
-        if self.max_position_embeddings > 0:
-            if position_ids is None:
-                position_ids = torch.arange(seqlen, dtype=torch.long, device=input_ids.device)
-            position_embeddings = self.position_embeddings(position_ids)
-            embeddings = embeddings + position_embeddings
-        return embeddings
-
-
-class NomicBertMLP(nn.Module):
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        activation=F.gelu,
-        bias1=True,
-        bias2=True,
-        return_residual=False,
-        fused_bias_fc=False,
-    ):
-        super().__init__()
-        out_features = out_features if out_features is not None else in_features
-        hidden_features = hidden_features if hidden_features is not None else in_features * 4
-        self.return_residual = return_residual
-        self.fc1 = nn.Linear(in_features, hidden_features, bias=bias1)
-        approximate = "tanh" if activation in ["gelu_new", "gelu_fast", "gelu_pytorch_tanh"] else "none"
-        self.activation = nn.GELU(approximate=approximate) if activation == "gelu" else activation
-        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias2)
-
-    def forward(self, x):
-        y = self.fc1(x)
-        y = self.activation(y)
-        y = self.fc2(y)
-        return y if not self.return_residual else (y, x)
-
-
-class NomciBertGatedMLP(nn.Module):
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        activation=F.sigmoid,
-        bias1=True,
-        bias2=True,
-        multiple_of=256,
-        return_residual=False,
-        fused_bias_fc=True,
-        device=None,
-        dtype=None,
-    ):
-        super().__init__()
-        out_features = out_features if out_features is not None else in_features
-        hidden_features = hidden_features if hidden_features is not None else int(8 * in_features / 3)
-        hidden_features = (hidden_features + multiple_of - 1) // multiple_of * multiple_of
-        self.return_residual = return_residual
-
-        self.fc11 = nn.Linear(in_features, hidden_features, bias=bias1)
-        self.fc12 = nn.Linear(in_features, hidden_features, bias=bias1)
-        self.activation = activation
-        self.fc2 = nn.Linear(hidden_features, out_features, bias=bias2)
-
-    def forward(self, x):
-        y = self.fc11(x)
-        gate = self.fc12(x)
-        if self.activation == F.sigmoid:  # Special case for GLU
-            y = F.glu(torch.cat([y, gate], dim=-1), dim=-1)
-        else:
-            y = y * self.activation(gate)
-        y = self.fc2(y)
-        return y if not self.return_residual else (y, x)
-
-
-def rotate_half(x, interleaved=False):
-    if not interleaved:
-        x1, x2 = x.chunk(2, dim=-1)
-        return torch.cat((-x2, x1), dim=-1)
-    else:
-        x1, x2 = x[..., ::2], x[..., 1::2]
-        return rearrange(torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2)
-
-
-def apply_rotary_emb(x, cos, sin, offset=0, interleaved=False):
-    """
-    x: (batch_size, seqlen, nheads, headdim)
-    cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2)
-    """
-    ro_dim = cos.shape[-1] * 2
-    assert ro_dim <= x.shape[-1]
-    cos, sin = (
-        cos[offset : offset + x.shape[1]],
-        sin[offset : offset + x.shape[1]],
-    )
-    cos = repeat(cos, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)")
-    sin = repeat(sin, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)")
-    return torch.cat(
-        [x[..., :ro_dim] * cos + rotate_half(x[..., :ro_dim], interleaved) * sin, x[..., ro_dim:]],
-        dim=-1,
-    )
-
-
-class NomicBertRotaryEmbedding(nn.Module):
-    def __init__(
-        self,
-        dim: int,
-        base=10000.0,
-        interleaved=False,
-        scale_base=None,
-        pos_idx_in_fp32=True,
-        device=None,
-    ):
-        """
-        interleaved: if True, rotate pairs of even and odd dimensions (GPT-J style) instead
-            of 1st half and 2nd half (GPT-NeoX style).
-        pos_idx_in_fp32: if True, the position indices [0.0, ..., seqlen - 1] are in fp32,
-            otherwise they might be in lower precision.
-            This option was added because previously (before 2023-07-02), when we construct
-            the position indices, we use the dtype of self.inv_freq. In most cases this would
-            be fp32, but if the model is trained in pure bf16 (not mixed precision), then
-            self.inv_freq would be bf16, and the position indices are also in bf16.
-            Because of the limited precision of bf16 (e.g. 1995.0 is rounded to 2000.0), the
-            embeddings for some positions will coincide.
-            To maintain compatibility with models previously trained in pure bf16,
-            we add this option.
-        """
-        super().__init__()
-        self.dim = dim
-        self.base = float(base)
-        self.pos_idx_in_fp32 = pos_idx_in_fp32
-        # Generate and save the inverse frequency buffer (non trainable)
-        inv_freq = self._compute_inv_freq(device)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.interleaved = interleaved
-        self.scale_base = scale_base
-        scale = (
-            (torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
-            if scale_base is not None
-            else None
-        )
-        self.register_buffer("scale", scale, persistent=False)
-
-        self._seq_len_cached = 0
-        self._cos_cached = None
-        self._sin_cached = None
-        self._cos_k_cached = None
-        self._sin_k_cached = None
-
-    def _compute_inv_freq(self, device=None):
-        return 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
-
-    def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):
-        # Reset the tables if the sequence length has changed,
-        # if we're on a new device (possibly due to tracing for instance),
-        # or if we're switching from inference mode to training
-        if (
-            seqlen > self._seq_len_cached
-            or self._cos_cached is None
-            or self._cos_cached.device != device
-            or self._cos_cached.dtype != dtype
-            or (self.training and self._cos_cached.is_inference())
-        ):
-            self._seq_len_cached = seqlen
-            # We want fp32 here, not self.inv_freq.dtype, since the model could be loaded in bf16
-            # And the output of arange can be quite large, so bf16 would lose a lot of precision.
-            # However, for compatibility reason, we add an option to use the dtype of self.inv_freq.
-            if self.pos_idx_in_fp32:
-                t = torch.arange(seqlen, device=device, dtype=torch.float32)
-                # We want fp32 here as well since inv_freq will be multiplied with t, and the output
-                # will be large. Having it in bf16 will lose a lot of precision and cause the
-                # cos & sin output to change significantly.
-                # We want to recompute self.inv_freq if it was not loaded in fp32
-                if self.inv_freq.dtype != torch.float32:
-                    inv_freq = self._compute_inv_freq(device=device)
-                else:
-                    inv_freq = self.inv_freq
-            else:
-                t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
-                inv_freq = self.inv_freq
-            # Don't do einsum, it converts fp32 to fp16 under AMP
-            # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-            freqs = torch.outer(t, inv_freq)
-            self._cos_cached = torch.cos(freqs).to(dtype)
-            self._sin_cached = torch.sin(freqs).to(dtype)
-
-    def forward(
-        self,
-        qkv: torch.Tensor,
-        kv: Optional[torch.Tensor] = None,
-        seqlen_offset: Union[int, torch.Tensor] = 0,
-        max_seqlen: Optional[int] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """
-        qkv: (batch, seqlen, 3, nheads, headdim) if kv is none,
-             else it's just q of shape (batch, seqlen, nheads, headdim)
-        kv: (batch, seqlen, 2, nheads, headdim)
-        seqlen_offset: (batch_size,) or int. Each sequence in x is shifted by this amount.
-            Most commonly used in inference when we have KV cache.
-            If it's a tensor of shape (batch_size,), then to update the cos / sin cache, one
-            should pass in max_seqlen, which will update the cos / sin cache up to that length.
-        Apply rotary embedding *inplace* to qkv and / or kv.
-        """
-        seqlen = qkv.shape[1]
-        if seqlen > self._seq_len_cached:
-            self._update_cos_sin_cache(seqlen, device=qkv.device, dtype=qkv.dtype)
-        elif max_seqlen is not None:
-            self._update_cos_sin_cache(max_seqlen, device=qkv.device, dtype=qkv.dtype)
-        elif isinstance(seqlen_offset, int):
-            self._update_cos_sin_cache(seqlen + seqlen_offset, device=qkv.device, dtype=qkv.dtype)
-
-        q_rot = apply_rotary_emb(qkv[:, :, 0], self._cos_cached, self._sin_cached, seqlen_offset, self.interleaved)
-        k_rot = apply_rotary_emb(qkv[:, :, 1], self._cos_cached, self._sin_cached, seqlen_offset, self.interleaved)
-        return torch.stack((q_rot, k_rot, qkv[:, :, 2]), dim=2)
-
-
-class NomicBertDynamicNTKRotaryEmbedding(NomicBertRotaryEmbedding):
-    def __init__(self, rotary_scaling_factor, max_position_embeddings, **kwargs):
-        super().__init__(**kwargs)
-        self.rotary_scaling_factor = rotary_scaling_factor
-        self.max_position_embeddings = max_position_embeddings
-
-    def _compute_inv_freq(self, base=None, device=None):
-        if base is None:
-            base = self.base
-        return 1.0 / (base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
-
-    def _update_cos_sin_cache(self, seqlen, device=None, dtype=None):
-        # Reset the tables if the sequence length has changed,
-        # if we're on a new device (possibly due to tracing for instance),
-        # or if we're switching from inference mode to training
-        if seqlen > self.max_position_embeddings:
-            base = self.base * (
-                (self.rotary_scaling_factor * seqlen / self.max_position_embeddings) - (self.rotary_scaling_factor - 1)
-            ) ** (self.dim / (self.dim - 2))
-            inv_freq = self._compute_inv_freq(base=base, device=device)
-            self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        if (
-            seqlen > self._seq_len_cached
-            or self._cos_cached is None
-            or self._cos_cached.device != device
-            or self._cos_cached.dtype != dtype
-            or (self.training and self._cos_cached.is_inference())
-        ):
-            self._seq_len_cached = seqlen
-            # We want fp32 here, not self.inv_freq.dtype, since the model could be loaded in bf16
-            # And the output of arange can be quite large, so bf16 would lose a lot of precision.
-            # However, for compatibility reason, we add an option to use the dtype of self.inv_freq.
-            if self.pos_idx_in_fp32:
-                t = torch.arange(seqlen, device=device, dtype=torch.float32)
-                # We want fp32 here as well since inv_freq will be multiplied with t, and the output
-                # will be large. Having it in bf16 will lose a lot of precision and cause the
-                # cos & sin output to change significantly.
-                # We want to recompute self.inv_freq if it was not loaded in fp32
-                if self.inv_freq.dtype != torch.float32:
-                    if seqlen > self.max_position_embeddings:
-                        base = self.base * (
-                            (self.scaling_factor * seqlen / self.max_position_embeddings) - (self.scaling_factor - 1)
-                        ) ** (self.dim / (self.dim - 2))
-                    else:
-                        base = self.base
-                    inv_freq = self._compute_inv_freq(device=device, base=base)
-                else:
-                    inv_freq = self.inv_freq
-            else:
-                t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
-                inv_freq = self.inv_freq
-            # Don't do einsum, it converts fp32 to fp16 under AMP
-            # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
-            freqs = torch.outer(t, inv_freq)
-            if self.scale is None:
-                self._cos_cached = torch.cos(freqs).to(dtype)
-                self._sin_cached = torch.sin(freqs).to(dtype)
-            else:
-                power = (
-                    torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2
-                ) / self.scale_base
-                scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1")
-                # We want the multiplication by scale to happen in fp32
-                self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
-                self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
-                self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
-                self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
-
-
-class NomicBertAttention(nn.Module):
-    """Multi-head self-attention and cross-attention"""
-
-    def __init__(
-        self,
-        config,
-    ) -> None:
-        """
-        num_heads_kv: can be used to toggle MQA / GQA. If None, use num_heads.
-        return_residual: whether to return the input x along with the output. This is for
-            performance reason: for post-norm architecture, returning the input allows us
-            to fuse the backward of nn.Linear with the residual connection.
-        """
-        super().__init__()
-        self.embed_dim = config.n_embd
-        self.use_flash_attn = config.use_flash_attn
-        self.fused_bias_fc = config.fused_bias_fc
-
-        self.num_heads = config.n_head
-        self.num_heads_kv = config.num_heads_kv if getattr(config, "num_heads_kv", None) is not None else self.num_heads
-        assert self.embed_dim % self.num_heads == 0, "embed_dim must be divisible by num_heads"
-        self.head_dim = self.embed_dim // self.num_heads
-        # we don't really support mqa / gqa for now
-        qkv_dim = self.head_dim * (self.num_heads + 2 * self.num_heads_kv)
-
-        self.register_buffer(
-            "norm_factor",
-            torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32)).to(torch.get_default_dtype()),
-            persistent=False,
-        )
-
-        self.rotary_emb_dim = self.head_dim * config.rotary_emb_fraction
-        if self.rotary_emb_dim > 0:
-            if getattr(config, "rotary_scaling_factor", None):
-                self.rotary_emb = NomicBertDynamicNTKRotaryEmbedding(
-                    dim=self.rotary_emb_dim,
-                    base=config.rotary_emb_base,
-                    scale_base=config.rotary_emb_scale_base,
-                    interleaved=config.rotary_emb_interleaved,
-                    rotary_scaling_factor=config.rotary_scaling_factor,
-                    max_position_embeddings=config.max_trained_positions,
-                )
-            else:
-                self.rotary_emb = NomicBertRotaryEmbedding(
-                    dim=self.rotary_emb_dim,
-                    base=config.rotary_emb_base,
-                    scale_base=config.rotary_emb_scale_base,
-                    interleaved=config.rotary_emb_interleaved,
-                )
-            # bug in xformers: https://github.com/facebookresearch/xformers/issues/841
-            # uses the head dimension instead of the sequence dimension
-            self.rotary_head_dim = getattr(config, "rotary_head_dim", False)
-
-        self.Wqkv = nn.Linear(self.embed_dim, qkv_dim, bias=config.qkv_proj_bias)
-
-        self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_proj_bias)
-        self.causal = config.causal
-        self.drop = nn.Dropout(config.attn_pdrop)
-
-    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,
-        is_padded_inputs: Optional[bool] = True,
-        cu_seqlens: Optional[torch.Tensor] = None,
-        max_seq_len: Optional[int] = None,
-    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-
-        has_layer_past = past_key_value is not None
-
-        if has_layer_past:
-            past_key_value = past_key_value[0]
-            past_len = past_key_value[1]
-        else:
-            past_len = 0
-
-        qkv = self.Wqkv(hidden_states)
-        qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)
-
-        past_key_value = (past_key_value, past_len + qkv.size(1)) if use_cache else None
-
-        if self.rotary_emb_dim > 0:
-            if self.rotary_head_dim:
-                qkv = rearrange(qkv, "b s three h d -> b h three s d")
-            qkv = self.rotary_emb(qkv, seqlen_offset=past_len)
-
-            if self.rotary_head_dim:
-                qkv = rearrange(qkv, "b h three s d -> b s three h d")
-
-        query, key, value = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
-
-        query = query.permute(0, 2, 1, 3)
-        key = key.permute(0, 2, 1, 3)
-        value = value.permute(0, 2, 1, 3)
-
-        attention_scores = torch.matmul(query, key.transpose(-1, -2)) / self.norm_factor
-        if attention_mask is not None:
-            attention_scores = attention_scores + attention_mask
-
-        attentions_probs = F.softmax(attention_scores, dim=-1)
-        attentions_probs = self.drop(attentions_probs)
-
-        attn_output = torch.matmul(attentions_probs, value)
-        attn_output = rearrange(attn_output.permute(0, 2, 1, 3), "... h d -> ... (h d)")
-
-        attn_output = self.out_proj(attn_output)
-
-        return attn_output
-
-
-class NomicBertBlock(nn.Module):
-    def __init__(
-        self,
-        config,
-    ):
-        super().__init__()
-        self.prenorm = config.prenorm
-        self.fused_dropout_add_ln = config.fused_dropout_add_ln
-
-        self.attn = NomicBertAttention(config)
-        activation = (
-            F.sigmoid
-            if config.activation_function == "glu"
-            else (F.silu if config.activation_function == "swiglu" else F.gelu)
-        )
-        if config.activation_function in ["glu", "swiglu", "geglu"]:
-            self.mlp = NomciBertGatedMLP(
-                config.n_embd,
-                hidden_features=config.n_inner,
-                bias1=config.mlp_fc1_bias,
-                bias2=config.mlp_fc2_bias,
-                activation=activation,
-                fused_bias_fc=config.fused_bias_fc,
-            )
-        else:
-            self.mlp = NomicBertMLP(
-                config.n_embd,
-                hidden_features=config.n_inner,
-                bias1=config.mlp_fc1_bias,
-                bias2=config.mlp_fc2_bias,
-                activation=activation,
-                fused_bias_fc=config.fused_bias_fc,
-            )
-
-        self.dropout1 = nn.Dropout(config.resid_pdrop)
-        self.norm1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
-        self.norm2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
-        self.dropout2 = nn.Dropout(config.resid_pdrop)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        hidden_states2: torch.Tensor,
-        residual: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        past_key_value: Optional[Tuple[torch.Tensor]] = None,
-        is_padded_inputs: Optional[bool] = True,
-        output_attentions: Optional[bool] = False,
-        use_cache: Optional[bool] = False,
-        cu_seqlens: Optional[torch.Tensor] = None,
-        max_seq_len: Optional[int] = None,
-    ):
-        r"""Pass the input through the encoder layer.
-
-        Args:
-            hidden_states: the sequence to the encoder layer (required).
-            residual: if postnorm, residual=None, If prenorm, hidden_states = Attn/MLP(LN(residual))
-            mixer_subset: for cross-attention only. If not None, will take a subset of x
-                before applying the query projection. Useful for e.g., ViT where we only care
-                about the CLS token in the last layer.
-        """
-        if self.prenorm:
-            dropped = self.dropout1(hidden_states)
-            residual = (dropped + residual) if residual is not None else dropped
-            hidden_states = self.norm1(residual.to(dtype=self.norm1.weight.dtype))
-            hidden_states = self.attn(
-                hidden_states,
-                attention_mask=attention_mask,
-                is_padded_inputs=is_padded_inputs,
-                cu_seqlens=cu_seqlens,
-                max_seq_len=max_seq_len,
-            )
-
-            dropped = self.dropout2(hidden_states)
-            residual = (dropped + residual) if residual is not None else dropped
-            hidden_states = self.norm2(residual.to(dtype=self.norm2.weight.dtype))
-            hidden_states = self.mlp(hidden_states)
-
-            return hidden_states, None, residual
-        else:
-            assert residual is None
-            attn_outputs = self.attn(
-                hidden_states,
-                attention_mask=attention_mask,
-                is_padded_inputs=is_padded_inputs,
-                cu_seqlens=cu_seqlens,
-                max_seq_len=max_seq_len,
-            )
-            hidden_states = self.norm1((self.dropout1(attn_outputs) + hidden_states).to(dtype=self.norm1.weight.dtype))
-            mlp_out = self.mlp(hidden_states)
-
-            hidden_states = self.norm2((self.dropout2(mlp_out) + hidden_states).to(dtype=self.norm2.weight.dtype))
-            return hidden_states, None, None
-
-
-class NomicBertEncoder(nn.Module):
-    def __init__(self, config: GPT2Config):
-        super().__init__()
-        self.layers = nn.ModuleList([NomicBertBlock(config) for _ in range(config.n_layer)])
-        self.gradient_checkpointing = False
-        self.config = config
-
-    def forward(
-        self,
-        hidden_states: 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,
-        is_padded_inputs: Optional[bool] = True,
-    ):
-        """If subset_mask is not None, we only want output for the subset of the sequence.
-        This means that we only compute the last layer output for these tokens.
-        subset_mask: (batch, seqlen), dtype=torch.bool
-        """
-        hidden_states2 = None
-        residual = None
-
-        for _, layer in enumerate(self.layers):
-            if self.gradient_checkpointing and self.training:
-
-                def create_custom_forward(module):
-                    def custom_forward(*inputs):
-                        # None for past_key_value
-                        return module(*inputs)
-
-                    return custom_forward
-
-                hidden_states, hidden_states2, residual = torch.utils.checkpoint.checkpoint(
-                    create_custom_forward(layer),
-                    hidden_states,
-                    hidden_states2,
-                    residual,
-                    attention_mask,
-                    None,
-                    None,
-                    is_padded_inputs,
-                    # if you freeze ANY layers, you need `use_reentrant=False`
-                    # https://github.com/huggingface/transformers/issues/21381
-                    # https://discuss.pytorch.org/t/checkpoint-with-no-grad-requiring-inputs-problem/19117/7
-                    use_reentrant=False,
-                )
-
-            else:
-                hidden_states, hidden_states2, residual = layer(
-                    hidden_states,
-                    hidden_states2,
-                    residual,
-                    attention_mask,
-                    position_ids,
-                    None,
-                    is_padded_inputs,
-                    output_attentions,
-                    use_cache,
-                )
-        return hidden_states
-
-
-class NomicBertPooler(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.n_embd, config.n_embd)
-        self.activation = nn.Tanh()
-
-    def forward(self, hidden_states, pool=True):
-        # We "pool" the model by simply taking the hidden state corresponding
-        # to the first token.
-        first_token_tensor = hidden_states[:, 0] if pool else hidden_states
-        pooled_output = self.dense(first_token_tensor)
-        pooled_output = self.activation(pooled_output)
-        return pooled_output
-
-
-class NomicBertPredictionHeadTransform(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.dense = nn.Linear(config.n_embd, config.n_embd, bias=config.mlp_fc1_bias)
-        approximate = "tanh" if config.activation_function in ["gelu_new", "gelu_fast", "gelu_pytorch_tanh"] else "none"
-        if config.activation_function == "swiglu":
-            self.transform_act_fn = F.silu
-        else:
-            self.transform_act_fn = nn.GELU(approximate=approximate)
-
-        self.layer_norm = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        hidden_states = self.dense(hidden_states)
-        hidden_states = self.transform_act_fn(hidden_states)
-        hidden_states = self.layer_norm(hidden_states)
-
-        return hidden_states
-
-
-class NomicBertLMPredictionHead(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-
-        self.transform = NomicBertPredictionHeadTransform(config)
-
-        self.decoder = nn.Linear(config.n_embd, config.vocab_size, bias=config.mlp_fc1_bias)
-
-    def forward(self, hidden_states):
-        hidden_states = self.transform(hidden_states)
-        hidden_states = self.decoder(hidden_states)
-        return hidden_states
-
-
-class NomicBertPreTrainingHeads(nn.Module):
-    def __init__(self, config):
-        super().__init__()
-        self.predictions = NomicBertLMPredictionHead(config)
-
-    def forward(self, sequence_output):
-        prediction_scores = self.predictions(sequence_output)
-        return prediction_scores
-
-
-class NomicBertModel(NomicBertPreTrainedModel):
-    def __init__(self, config: GPT2Config, add_pooling_layer=True):
-        super().__init__(config)
-        self.pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
-        if config.vocab_size % self.pad_vocab_size_multiple != 0:
-            config.vocab_size += self.pad_vocab_size_multiple - (config.vocab_size % self.pad_vocab_size_multiple)
-
-        assert config.activation_function in [
-            "gelu",
-            "gelu_new",
-            "gelu_fast",
-            "gelu_pytorch_tanh",
-            "swiglu",
-            "geglu",
-            "glu",
-        ]
-
-        self.embeddings = NomicBertEmbeddings(config)
-        self.emb_drop = nn.Dropout(config.resid_pdrop)
-        self.emb_ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
-        self.encoder = NomicBertEncoder(config)
-        self.pooler = NomicBertPooler(config) if add_pooling_layer else None
-
-        self.apply(partial(_init_weights, initializer_range=config.initializer_range))
-
-    def forward(
-        self,
-        input_ids,
-        position_ids=None,
-        token_type_ids=None,
-        attention_mask=None,
-        return_dict=None,
-        matryoshka_dim=None,
-    ):
-        if token_type_ids is None:
-            token_type_ids = torch.zeros_like(input_ids)
-        hidden_states = self.embeddings(input_ids, position_ids=position_ids, token_type_ids=token_type_ids)
-        hidden_states = self.emb_ln(hidden_states)
-        hidden_states = self.emb_drop(hidden_states)
-
-        attention_mask = self.get_extended_attention_mask(attention_mask, input_ids.shape)
-        sequence_output = self.encoder(hidden_states, attention_mask=attention_mask, return_dict=return_dict)
-
-        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
-
-        if matryoshka_dim:
-            sequence_output = sequence_output[:, :matryoshka_dim]
-
-        return BaseModelOutputWithPoolingAndCrossAttentions(
-            last_hidden_state=sequence_output,
-            pooler_output=pooled_output,
-        )
-
-
-class NomicBertForPreTraining(NomicBertPreTrainedModel):
-    _tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]
-
-    def __init__(self, config: GPT2Config):
-        super().__init__(config)
-
-        self.bert = NomicBertModel(config, add_pooling_layer=getattr(config, "add_pooling_layer", False))
-        self.cls = NomicBertPreTrainingHeads(config)
-        self.mlm_loss = nn.CrossEntropyLoss()
-
-        # Initialize weights and apply final processing
-        self.apply(partial(_init_weights, initializer_range=config.initializer_range))
-        self.tie_weights()
-
-    def tie_weights(self):
-        self.cls.predictions.decoder.weight = self.bert.embeddings.word_embeddings.weight
-
-    def forward(
-        self,
-        input_ids,
-        position_ids=None,
-        token_type_ids=None,
-        attention_mask=None,
-        labels=None,
-    ):
-        """
-        If labels are provided, they must be -100 for masked out tokens (as specified in the attention
-        mask).
-        Outputs:
-            if `labels` and `next_sentence_label` are not `None`:
-                Outputs the total_loss which is the sum of the masked language modeling loss and the next
-                sentence classification loss.
-            if `labels` or `next_sentence_label` is `None`:
-                Outputs a tuple comprising
-                - the masked language modeling logits of shape [batch_size, sequence_length, vocab_size], and
-                - the next sentence classification logits of shape [batch_size, 2].
-
-        """
-        outputs = self.bert(
-            input_ids,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            attention_mask=attention_mask.bool() if attention_mask is not None else None,
-        )
-        sequence_output, _ = outputs.last_hidden_state, outputs.pooler_output
-
-        prediction_scores = self.cls(sequence_output)
-
-        total_loss = None
-        if labels is not None:
-            masked_lm_loss = self.mlm_loss(
-                rearrange(prediction_scores, "... v -> (...) v"),
-                rearrange(labels, "... -> (...)"),
-            )
-            total_loss = masked_lm_loss.float()
-
-        return MaskedLMOutput(
-            loss=total_loss,
-            logits=prediction_scores,
-            hidden_states=outputs.hidden_states,
-            attentions=None,
-        )
-
-
-class NomicBertForSequenceClassification(NomicBertPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.config = config
-
-        self.bert = NomicBertModel(config)
-        classifier_dropout = getattr(config, "classifier_dropout", config.embd_pdrop)
-        self.dropout = nn.Dropout(classifier_dropout)
-        self.classifier = nn.Linear(config.n_embd, config.num_labels)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def forward(
-        self,
-        input_ids: Optional[torch.Tensor] = None,
-        attention_mask: Optional[torch.Tensor] = None,
-        token_type_ids: Optional[torch.Tensor] = None,
-        position_ids: Optional[torch.Tensor] = None,
-        head_mask: Optional[torch.Tensor] = None,
-        inputs_embeds: Optional[torch.Tensor] = None,
-        labels: Optional[torch.Tensor] = None,
-        output_attentions: Optional[bool] = None,
-        output_hidden_states: Optional[bool] = None,
-        return_dict: Optional[bool] = None,
-    ):
-        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
-        outputs = self.bert(
-            input_ids,
-            position_ids=position_ids,
-            token_type_ids=token_type_ids,
-            attention_mask=attention_mask.bool() if attention_mask is not None else None,
-        )
-
-        pooled_output = outputs[1]
-
-        pooled_output = self.dropout(pooled_output)
-        logits = self.classifier(pooled_output)
-
-        loss = None
-        if labels is not None:
-            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 = nn.MSELoss()
-                if self.num_labels == 1:
-                    loss = loss_fct(logits.squeeze(), labels.squeeze())
-                else:
-                    loss = loss_fct(logits, labels)
-            elif self.config.problem_type == "single_label_classification":
-                loss_fct = nn.CrossEntropyLoss()
-                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
-            elif self.config.problem_type == "multi_label_classification":
-                loss_fct = nn.BCEWithLogitsLoss()
-                loss = loss_fct(logits, labels)
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
-        return SequenceClassifierOutput(
-            loss=loss,
-            logits=logits,
-            hidden_states=outputs.hidden_states,
-            attentions=outputs.attentions,
-        )