Upload 9 files

bert_padding.py

@@ -0,0 +1,156 @@
+# Copyright 2022 MosaicML Examples authors
+# SPDX-License-Identifier: Apache-2.0
+
+# Adapted from https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/bert_padding.py
+# Which was adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/padding.py
+
+"""Helper functions for padding and unpadding batches.  """
+
+from typing import Tuple, cast
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange, repeat
+
+
+class IndexFirstAxis(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input: torch.Tensor, indices: torch.Tensor) -> torch.Tensor:
+        """Get just the values of `input` which are at `indices`.
+
+        Arguments:
+            ctx: the autograd context object
+            input: (b, ...) 2+ dimensional tensor
+            indices: (num_idx) 1D tensor
+        """
+        ctx.save_for_backward(indices)
+        assert input.ndim >= 2
+        ctx.first_axis_dim, other_shape = (
+            input.shape[0],
+            input.shape[1:],
+        )  # type: ignore
+        second_dim = other_shape.numel()  # product of sizes of all but first dimension
+        # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
+        return torch.gather(
+            rearrange(input, 'b ... -> b (...)'),  # (b, ...) -> (b, second_dim)
+            0,
+            repeat(
+                indices, 'z -> z d', d=second_dim
+            ),  # (indices,) -> (indices, second_dim)
+        ).reshape(
+            -1, *other_shape
+        )  # (num_idx, ...)
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> Tuple[torch.Tensor, None]:
+        (indices,) = ctx.saved_tensors
+        assert grad_output.ndim >= 2
+        other_shape = grad_output.shape[1:]
+        grad_output = rearrange(grad_output, 'b ... -> b (...)')
+        grad_input = torch.zeros(
+            [ctx.first_axis_dim, grad_output.shape[1]],
+            device=grad_output.device,
+            dtype=grad_output.dtype,
+        )
+        # TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing.
+        # grad_input[indices] = grad_output
+        grad_input.scatter_(
+            0, repeat(indices, 'z -> z d', d=grad_output.shape[1]), grad_output
+        )
+        return grad_input.reshape(ctx.first_axis_dim, *other_shape), None
+
+
+index_first_axis = IndexFirstAxis.apply
+
+
+class IndexPutFirstAxis(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx, values: torch.Tensor, indices: torch.Tensor, first_axis_dim
+    ) -> torch.Tensor:
+        ctx.save_for_backward(indices)
+        assert indices.ndim == 1
+        assert values.ndim >= 2
+        output = torch.zeros(
+            first_axis_dim, *values.shape[1:], device=values.device, dtype=values.dtype
+        )
+        output[indices] = values
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output: torch.Tensor) -> Tuple[torch.Tensor, None, None]:
+        (indices,) = ctx.saved_tensors
+        grad_values = grad_output[indices]
+        return grad_values, None, None
+
+
+index_put_first_axis = IndexPutFirstAxis.apply
+
+
+def unpad_input(
+    hidden_states: torch.Tensor,
+    attention_mask: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, int]:
+    """Remove padding from input sequences.
+
+    Arguments:
+        hidden_states: (batch, seqlen, ...)
+        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
+
+    Returns:
+        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
+        indices: (total_nnz)
+        cu_seqlens: (batch + 1), the cumulative sequence lengths, used to index into hidden_states.
+        max_seqlen_in_batch: int ()
+    """
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = int(seqlens_in_batch.max().item())
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+    # TD [2022-03-04] We don't want to index with a bool mask, because Pytorch will expand the
+    # bool mask, then call nonzero to get the indices, then index with those. The indices is @dim
+    # times larger than it needs to be, wasting memory. It's faster and more memory-efficient to
+    # index with integer indices. Moreover, torch's index is a bit slower than it needs to be,
+    # so we write custom forward and backward to make it a bit faster.
+    hidden_states = cast(
+        torch.Tensor,
+        index_first_axis(rearrange(hidden_states, 'b s ... -> (b s) ...'), indices),
+    )
+    return hidden_states, indices, cu_seqlens, max_seqlen_in_batch
+
+
+def unpad_input_only(
+    hidden_states: torch.Tensor,
+    attention_mask: torch.Tensor,
+) -> torch.Tensor:
+    """Like unpad_input, but only return the unpadded first tensor.
+
+    Save a small amount of overhead.
+
+    Arguments:
+        hidden_states: (batch, seqlen, ...)
+        attention_mask: (batch, seqlen), bool / int, 1 means valid and 0 means not valid.
+
+    Returns:
+        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
+    """
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    return index_first_axis(rearrange(hidden_states, 'b s ... -> (b s) ...'), indices)
+
+
+def pad_input(
+    hidden_states: torch.Tensor, indices: torch.Tensor, batch: int, seqlen: int
+) -> torch.Tensor:
+    """Add padding to sequences.
+
+    Arguments:
+        hidden_states: (total_nnz, ...), where total_nnz = number of tokens in selected in attention_mask.
+        indices: (total_nnz)
+        batch: int batch_size
+        seqlen: int max sequence length
+
+    Returns:
+        hidden_states: (batch, seqlen, ...)
+    """
+    output = index_put_first_axis(hidden_states, indices, batch * seqlen)
+    return rearrange(output, '(b s) ... -> b s ...', b=batch)

config.json

@@ -0,0 +1,33 @@
+{
+  "_name_or_path": "jinaai/jina-bert-s-en-v1",
+  "model_max_length": 512,
+  "architectures": [
+    "JBertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "auto_map": {
+    "AutoConfig": "configuration_jbert.JBertConfig",
+    "AutoModelForMaskedLM": "modeling_jbert.JBertForMaskedLM",
+    "AutoModel": "modeling_jbert.JBertModel",
+    "AutoModelForSequenceClassification": "modeling_jbert.JBertForSequenceClassification"
+  },
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 4,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.26.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30528
+}

configuration_jbert.py

@@ -0,0 +1,26 @@
+# Copyright 2022 MosaicML Examples authors
+# SPDX-License-Identifier: Apache-2.0
+
+from transformers import BertConfig as TransformersBertConfig
+
+
+class JBertConfig(TransformersBertConfig):
+    def __init__(
+        self,
+        model_max_length: int = 8192,
+        attention_probs_dropout_prob: float = 0.0,
+        **kwargs,
+    ):
+        """Configuration class for MosaicBert.
+
+        Args:
+            model_max_length (int): Use `model_max_length` to determine how large of an alibi tensor to
+                create when initializing the model. You should be able to ignore this parameter in most cases.
+                Defaults to 8192.
+            attention_probs_dropout_prob (float): By default, turn off attention dropout in Mosaic BERT
+                (otherwise, Flash Attention will be off by default). Defaults to 0.0.
+        """
+        super().__init__(
+            attention_probs_dropout_prob=attention_probs_dropout_prob, **kwargs
+        )
+        self.model_max_length = model_max_length

generation_config.json

@@ -0,0 +1,5 @@
+{
+  "_from_model_config": true,
+  "pad_token_id": 0,
+  "transformers_version": "4.26.0"
+}

gitattributes.txt

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

modeling_jbert.py

@@ -0,0 +1,908 @@
+# Copyright 2022 MosaicML Examples authors
+# SPDX-License-Identifier: Apache-2.0
+
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018-2021, NVIDIA CORPORATION.  All rights reserved.
+# Copyright (c) 2022, Tri Dao.
+
+import copy
+import logging
+import math
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from einops import rearrange
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+    MaskedLMOutput,
+    SequenceClassifierOutput,
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+)
+from transformers.models.bert.modeling_bert import BertPreTrainedModel
+
+from .bert_padding import (index_first_axis, index_put_first_axis, pad_input,
+                           unpad_input, unpad_input_only)
+from .configuration_jbert import JBertConfig
+
+logger = logging.getLogger(__name__)
+
+
+class JBertEmbeddings(nn.Module):
+    """Construct the embeddings for words, ignoring position.
+
+    There are no positional embeddings since we use ALiBi and token_type
+    embeddings.
+
+    This module is modeled after the Hugging Face BERT's
+    :class:`~transformers.model.bert.modeling_bert.BertEmbeddings`, but is
+    modified to implement ALiBi. The key change is
+    that position embeddings are removed. Position information instead comes
+    from attention biases that scale linearly with the position distance
+    between query and key tokens.
+
+    This module ignores the `position_ids` input to the `forward` method.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.word_embeddings = nn.Embedding(
+            config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
+        )
+        # ALiBi doesn't use position embeddings
+        self.token_type_embeddings = nn.Embedding(
+            config.type_vocab_size, config.hidden_size
+        )
+
+        # self.LayerNorm is not snake-cased to stick with TensorFlow model
+        # variable name and be able to load any TensorFlow checkpoint file
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.register_buffer(
+            "token_type_ids", torch.zeros((1, config.model_max_length), dtype=torch.long), persistent=False
+        )
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        past_key_values_length: int = 0,
+    ) -> torch.Tensor:
+        if (input_ids is not None) == (inputs_embeds is not None):
+            raise ValueError('Must specify either input_ids or input_embeds!')
+        if input_ids is not None:
+            input_shape = input_ids.size()
+        else:
+            assert inputs_embeds is not None  # just for type checking
+            input_shape = inputs_embeds.size()[:-1]
+
+        seq_length = input_shape[1]
+
+        if position_ids is not None:
+            warnings.warn('position_ids is not used in JBertEmbeddings as it does not have position embeddings.')
+
+        # Setting the token_type_ids to the registered buffer in constructor
+        # where it is all zeros, which usually occurs when it's auto-generated;
+        # registered buffer helps users when tracing the model without passing
+        # token_type_ids, solves issue #5664
+        if token_type_ids is None:
+            if hasattr(self, 'token_type_ids'):
+                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
+                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
+                    input_shape[0], seq_length
+                )
+                token_type_ids = buffered_token_type_ids_expanded  # type: ignore
+            else:
+                token_type_ids = torch.zeros(
+                    input_shape,  # type: ignore
+                    dtype=torch.long,
+                    device=self.word_embeddings.device,
+                )  # type: ignore  # yapf: disable
+
+        if inputs_embeds is None:
+            inputs_embeds = self.word_embeddings(input_ids)
+        token_type_embeddings = self.token_type_embeddings(token_type_ids)
+
+        embeddings = inputs_embeds + token_type_embeddings
+        embeddings = self.LayerNorm(embeddings)
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class BertUnpadSelfAttention(nn.Module):
+    """Performs multi-headed self attention on a batch of unpadded sequences.
+
+    If Triton is installed, this module uses Flash Attention to greatly improve throughput.
+    The Flash Attention implementation used in Mosaic BERT supports arbitrary attention biases (which
+    we use to implement ALiBi), but does not support attention dropout. If either Triton is not installed
+    or `config.attention_probs_dropout_prob > 0`, the implementation will default to a
+    math-equivalent pytorch version, which is much slower.
+
+    See `forward` method for additional detail.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
+            config, 'embedding_size'
+        ):
+            raise ValueError(
+                f'The hidden size ({config.hidden_size}) is not a multiple of the number of attention '
+                f'heads ({config.num_attention_heads})'
+            )
+
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        # TODO: self.all_head_size == config.hidden_size? Why not just use config.hidden_size?
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.Wqkv = nn.Linear(self.all_head_size, 3 * config.hidden_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        max_seqlen_in_batch: int,
+        indices: torch.Tensor,
+        attn_mask: torch.Tensor,
+        bias: torch.Tensor,
+    ) -> torch.Tensor:
+        """Perform self-attention.
+
+        If dropout is zero, then we can use the Triton kernel, so we do that. However, if not, we send through a standard PyTorch
+        implementation of self-attention.
+
+        The arguments are unpadded, and our implementations of attention require padded arguments,
+        so we first call `pad_input`. Once we compute attention, we re-unpad our outputs for the other layers.
+        The pad/unpad operations add overhead, but not sending pad tokens through ffs saves compute.
+        It is possible to write an unpadded implementation of attention (in Triton and PyTorch), which we will eventually do.
+
+        Args:
+            hidden_states: (total_nnz, dim)
+            cu_seqlens: (batch + 1,)
+            max_seqlen_in_batch: int
+            indices: (total_nnz,)
+            attn_mask: (batch, max_seqlen_in_batch)
+            bias: (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
+
+        Returns:
+            attention: (total_nnz, dim)
+        """
+        qkv = self.Wqkv(hidden_states)
+        qkv = pad_input(
+            qkv, indices, cu_seqlens.shape[0] - 1, max_seqlen_in_batch
+        )  # batch, max_seqlen_in_batch, thd
+        qkv = rearrange(
+            qkv, 'b s (t h d) -> b s t h d', t=3, h=self.num_attention_heads
+        )
+        # if we have nonzero attention dropout (e.g. during fine-tuning) or no Triton, compute attention in PyTorch
+        q = qkv[:, :, 0, :, :].permute(0, 2, 1, 3)  # b h s d
+        k = qkv[:, :, 1, :, :].permute(0, 2, 3, 1)  # b h d s
+        v = qkv[:, :, 2, :, :].permute(0, 2, 1, 3)  # b h s d
+        attention_scores = torch.matmul(q, k) / math.sqrt(self.attention_head_size)
+        attention_scores = attention_scores + bias
+        attention_probs = nn.functional.softmax(attention_scores, dim=-1)
+        attention_probs = self.dropout(attention_probs)
+        attention_probs = attention_probs.to(dtype=v.dtype)
+        attention = torch.matmul(attention_probs, v).permute(0, 2, 1, 3)  # b s h
+
+        # attn_mask is 1 for attend and 0 for don't
+        attention = unpad_input_only(attention, torch.squeeze(attn_mask) == 1)
+        return rearrange(attention, 'nnz h d -> nnz (h d)')
+
+
+# Copy of transformer's library BertSelfOutput that will not be caught by surgery methods looking for HF BERT modules.
+class BertSelfOutput(nn.Module):
+    """Computes the output of the attention layer.
+
+    This module is modeled after the Hugging Face BERT's
+    :class:`~transformers.model.bert.modeling_bert.BertSelfOutput`.
+    The implementation is identical. Rather than use the original module
+    directly, we re-implement it here so that Mosaic BERT's modules will not
+    be affected by any Composer surgery algorithm that modifies Hugging Face
+    BERT modules.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(
+        self, hidden_states: torch.Tensor, input_tensor: torch.Tensor
+    ) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = self.LayerNorm(hidden_states + input_tensor)
+        return hidden_states
+
+
+class BertUnpadAttention(nn.Module):
+    """Chains attention, Dropout, and LayerNorm for Mosaic BERT."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.self = BertUnpadSelfAttention(config)
+        self.output = BertSelfOutput(config)
+
+    def forward(
+        self,
+        input_tensor: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        max_s: int,
+        subset_idx: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        attn_mask: Optional[torch.Tensor] = None,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Forward pass for scaled self-attention without padding.
+
+        Arguments:
+            input_tensor: (total_nnz, dim)
+            cu_seqlens: (batch + 1,)
+            max_s: int
+            subset_idx: () set of indices whose values we care about at the end of the layer
+                        (e.g., the masked tokens, if this is the final layer).
+            indices: None or (total_nnz,)
+            attn_mask: None or (batch, max_seqlen_in_batch)
+            bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
+        """
+        self_output = self.self(
+            input_tensor, cu_seqlens, max_s, indices, attn_mask, bias
+        )
+        if subset_idx is not None:
+            return self.output(
+                index_first_axis(self_output, subset_idx),
+                index_first_axis(input_tensor, subset_idx),
+            )
+        else:
+            return self.output(self_output, input_tensor)
+
+
+class BertGatedLinearUnitMLP(nn.Module):
+    """Applies the FFN at the end of each Mosaic BERT layer.
+
+    Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
+    and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality, but
+    introduces Gated Linear Units.
+
+    Note: Mosaic BERT adds parameters in order to implement Gated Linear Units. To keep parameter count consistent with that of a
+    standard Hugging Face BERT, scale down `config.intermediate_size` by 2/3. For example, a Mosaic BERT constructed with
+    `config.intermediate_size=2048` will have the same parameter footprint as its Hugging Face BERT counterpart constructed
+    with the `config.intermediate_size=3072`.
+    However, in most cases it will not be necessary to adjust `config.intermediate_size` since, despite the increased
+    parameter size, Mosaic BERT typically offers a net higher throughput than a Hugging Face BERT built from the same `config`.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.gated_layers = nn.Linear(
+            config.hidden_size, config.intermediate_size * 2, bias=False
+        )
+        self.act = nn.GELU(approximate='none')
+        self.wo = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """Compute new hidden states from current hidden states.
+
+        Args:
+            hidden_states (torch.Tensor): The (unpadded) hidden states from
+                the attention layer [nnz, dim].
+        """
+        residual_connection = hidden_states
+        # compute the activation
+        hidden_states = self.gated_layers(hidden_states)
+        gated = hidden_states[:, : self.config.intermediate_size]
+        non_gated = hidden_states[:, self.config.intermediate_size :]
+        hidden_states = self.act(gated) * non_gated
+        hidden_states = self.dropout(hidden_states)
+        # multiply by the second matrix
+        hidden_states = self.wo(hidden_states)
+        # add the residual connection and post-LN
+        hidden_states = self.layernorm(hidden_states + residual_connection)
+        return hidden_states
+
+
+class BertLayer(nn.Module):
+    """Composes the Mosaic BERT attention and FFN blocks into a single layer."""
+
+    def __init__(self, config: JBertConfig):
+        super().__init__()
+        self.attention = BertUnpadAttention(config)
+        self.mlp = BertGatedLinearUnitMLP(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        seqlen: int,
+        subset_idx: Optional[torch.Tensor] = None,
+        indices: Optional[torch.Tensor] = None,
+        attn_mask: Optional[torch.Tensor] = None,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """Forward pass for a BERT layer, including both attention and MLP.
+
+        Args:
+            hidden_states: (total_nnz, dim)
+            cu_seqlens: (batch + 1,)
+            seqlen: int
+            subset_idx: () set of indices whose values we care about at the end of the layer
+                        (e.g., the masked tokens, if this is the final layer).
+            indices: None or (total_nnz,)
+            attn_mask: None or (batch, max_seqlen_in_batch)
+            bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
+        """
+        attention_output = self.attention(
+            hidden_states, cu_seqlens, seqlen, subset_idx, indices, attn_mask, bias
+        )
+        layer_output = self.mlp(attention_output)
+        return layer_output
+
+
+class JBertEncoder(nn.Module):
+    """A stack of BERT layers providing the backbone.
+
+    This module is modeled after the Hugging Face BERT's :class:`~transformers.model.bert.modeling_bert.BertEncoder`,
+    but with substantial modifications to implement unpadding and ALiBi.
+
+    Compared to the analogous Hugging Face BERT module, this module handles unpadding to reduce unnecessary computation
+    at padded tokens, and pre-computes attention biases to implement ALiBi.
+    """
+
+    def __init__(self, config: JBertConfig):
+        super().__init__()
+        self.layer = nn.ModuleList(
+            [BertLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+
+        self.num_attention_heads = config.num_attention_heads
+
+        # The alibi mask will be dynamically expanded if it is too small for
+        # the input the model receives. But it generally helps to initialize it
+        # to a reasonably large size to help pre-allocate CUDA memory.
+        # The default `model_max_length` is 8192.
+        self._current_alibi_size = int(config.model_max_length)
+        self.alibi = torch.zeros(
+            (
+                1,
+                self.num_attention_heads,
+                self._current_alibi_size,
+                self._current_alibi_size,
+            )
+        )
+        self.rebuild_alibi_tensor(size=config.model_max_length)
+
+    def rebuild_alibi_tensor(
+        self, size: int, device: Optional[Union[torch.device, str]] = None
+    ):
+        # Alibi
+        # Following https://github.com/ofirpress/attention_with_linear_biases/issues/5 (Implementation 1)
+        # In the causal case, you can exploit the fact that softmax is invariant to a uniform translation
+        # of the logits, which makes the math work out *after* applying causal masking. If no causal masking
+        # will be applied, it is necessary to construct the diagonal mask.
+        n_heads = self.num_attention_heads
+
+        def _get_alibi_head_slopes(n_heads: int) -> List[float]:
+            def get_slopes_power_of_2(n_heads: int) -> List[float]:
+                start = 2 ** (-(2 ** -(math.log2(n_heads) - 3)))
+                ratio = start
+                return [start * ratio**i for i in range(n_heads)]
+
+            # In the paper, they only train models that have 2^a heads for some a. This function
+            # has some good properties that only occur when the input is a power of 2. To
+            # maintain that even when the number of heads is not a power of 2, we use a
+            # workaround.
+            if math.log2(n_heads).is_integer():
+                return get_slopes_power_of_2(n_heads)
+
+            closest_power_of_2 = 2 ** math.floor(math.log2(n_heads))
+            slopes_a = get_slopes_power_of_2(closest_power_of_2)
+            slopes_b = _get_alibi_head_slopes(2 * closest_power_of_2)
+            slopes_b = slopes_b[0::2][: n_heads - closest_power_of_2]
+            return slopes_a + slopes_b
+
+        context_position = torch.arange(size, device=device)[:, None]
+        memory_position = torch.arange(size, device=device)[None, :]
+        relative_position = torch.abs(memory_position - context_position)
+        # [n_heads, max_token_length, max_token_length]
+        relative_position = relative_position.unsqueeze(0).expand(n_heads, -1, -1)
+        slopes = torch.Tensor(_get_alibi_head_slopes(n_heads)).to(device)
+        alibi = slopes.unsqueeze(1).unsqueeze(1) * -relative_position
+        # [1, n_heads, max_token_length, max_token_length]
+        alibi = alibi.unsqueeze(0)
+        assert alibi.shape == torch.Size([1, n_heads, size, size])
+
+        self._current_alibi_size = size
+        self.alibi = alibi
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+    ) -> List[torch.Tensor]:
+        all_hidden_states = [] if output_hidden_states else None
+
+        extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+        attention_mask_bool = attention_mask.bool()
+        batch, seqlen = hidden_states.shape[:2]
+        # Unpad inputs and mask. It will remove tokens that are padded.
+        # Assume ntokens is total number of tokens (padded and non-padded)
+        # and ntokens_unpad is total number of non-padded tokens.
+        # Then unpadding performs the following compression of the inputs:
+        # hidden_states[ntokens,hidden] -> hidden_states[ntokens_unpad,hidden]
+        hidden_states, indices, cu_seqlens, _ = unpad_input(
+            hidden_states, attention_mask_bool
+        )
+
+        # Add alibi matrix to extended_attention_mask
+        if self._current_alibi_size < seqlen:
+            # Rebuild the alibi tensor when needed
+            warnings.warn(
+                f'Increasing alibi size from {self._current_alibi_size} to {seqlen}'
+            )
+            self.rebuild_alibi_tensor(size=seqlen, device=hidden_states.device)
+        elif self.alibi.device != hidden_states.device:
+            # Device catch-up
+            self.alibi = self.alibi.to(hidden_states.device)
+        alibi_bias = self.alibi[:, :, :seqlen, :seqlen]
+        attn_bias = extended_attention_mask[:, :, :seqlen, :seqlen]
+        alibi_attn_mask = attn_bias + alibi_bias
+
+        for layer_module in self.layer:
+            if output_hidden_states:
+                all_hidden_states.append(rearrange(hidden_states, '(b n) d -> b n d', b=batch))
+            hidden_states = layer_module(
+                hidden_states,
+                cu_seqlens,
+                seqlen,
+                None,
+                indices,
+                attn_mask=attention_mask,
+                bias=alibi_attn_mask,
+            )
+        # Pad inputs and mask. It will insert back zero-padded tokens.
+        # Assume ntokens is total number of tokens (padded and non-padded)
+        # and ntokens_unpad is total number of non-padded tokens.
+        # Then padding performs the following de-compression:
+        #     hidden_states[ntokens_unpad,hidden] -> hidden_states[ntokens,hidden]
+        hidden_states = pad_input(hidden_states, indices, batch, seqlen)
+
+        if output_hidden_states:
+            all_hidden_states.append(hidden_states)
+        
+        if not return_dict:
+            return tuple(
+                v for v in [hidden_states, all_hidden_states] if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=None,
+            hidden_states=all_hidden_states,
+            attentions=None,
+            cross_attentions=None,
+        )
+
+
+class JBertPooler(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.activation = nn.Tanh()
+
+    def forward(
+        self, hidden_states: torch.Tensor, pool: Optional[bool] = True
+    ) -> torch.Tensor:
+        # 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 BertPredictionHeadTransform(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        if isinstance(config.hidden_act, str):
+            self.transform_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.transform_act_fn = config.hidden_act
+        self.LayerNorm = torch.nn.LayerNorm(config.hidden_size, eps=1e-12)
+
+    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.LayerNorm(hidden_states)
+        return hidden_states
+
+
+class JBertModel(BertPreTrainedModel):
+    """Overall BERT model.
+
+    Args:
+        config: a JBertConfig class instance with the configuration to build a new model
+
+    Inputs:
+        `input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
+            with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
+            `extract_features.py`, `run_classifier.py` and `run_squad.py`)
+        `token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
+            types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
+            a `sentence B` token (see BERT paper for more details).
+        `attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
+            selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
+            input sequence length in the current batch. It's the mask that we typically use for attention when
+            a batch has varying length sentences.
+        `output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
+
+    Outputs: Tuple of (encoded_layers, pooled_output)
+        `encoded_layers`: controlled by `output_all_encoded_layers` argument:
+            - `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
+                of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
+                encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
+            - `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
+                to the last attention block of shape [batch_size, sequence_length, hidden_size],
+        `pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
+            classifier pretrained on top of the hidden state associated to the first character of the
+            input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
+
+    Example usage:
+    ```python
+    # Already been converted into WordPiece token ids
+    input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
+    input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
+    token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
+    config = modeling.JBertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
+        num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
+    model = JBertModel(config=config)
+    all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
+    ```
+    """
+
+    config_class = JBertConfig
+
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config)
+        self.embeddings = JBertEmbeddings(config)
+        self.encoder = JBertEncoder(config)
+        self.pooler = JBertPooler(config) if add_pooling_layer else None
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        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,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros_like(input_ids)
+
+        embedding_output = self.embeddings(input_ids, token_type_ids, position_ids)
+
+        encoder_outputs: BaseModelOutputWithPastAndCrossAttentions = self.encoder(
+            hidden_states=embedding_output,
+            attention_mask=attention_mask,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = encoder_outputs[0]
+        pooled_output = (
+            self.pooler(sequence_output) if self.pooler is not None else None
+        )
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        #return encoder_outputs, None
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+
+###################
+# Bert Heads
+###################
+class BertLMPredictionHead(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super().__init__()
+        self.transform = BertPredictionHeadTransform(config)
+        # The output weights are the same as the input embeddings, but there is
+        # an output-only bias for each token.
+        self.decoder = nn.Linear(
+            bert_model_embedding_weights.size(1), bert_model_embedding_weights.size(0)
+        )
+        self.decoder.weight = bert_model_embedding_weights
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.transform(hidden_states)
+        hidden_states = self.decoder(hidden_states)
+        return hidden_states
+
+
+class BertOnlyMLMHead(nn.Module):
+    def __init__(self, config, bert_model_embedding_weights):
+        super().__init__()
+        self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
+
+    def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
+        prediction_scores = self.predictions(sequence_output)
+        return prediction_scores
+
+
+class BertOnlyNSPHead(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+    def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
+        seq_relationship_score = self.seq_relationship(pooled_output)
+        return seq_relationship_score
+
+
+#####################
+# Various Bert models
+#####################
+class JBertForMaskedLM(BertPreTrainedModel):
+    config_class = JBertConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+
+        if config.is_decoder:
+            warnings.warn(
+                'If you want to use `JBertForMaskedLM` make sure `config.is_decoder=False` for '
+                'bi-directional self-attention.'
+            )
+
+        self.bert = JBertModel(config, add_pooling_layer=False)
+        self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.cls.predictions.decoder
+
+    def set_output_embeddings(self, new_embeddings):
+        self.cls.predictions.decoder = new_embeddings
+
+    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,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        encoder_attention_mask: 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,
+    ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
+        # labels should be a `torch.LongTensor` of shape
+        # `(batch_size, sequence_length)`. These are used for computing the
+        #  masked language modeling loss.
+        #
+        # Indices should be in `[-100, 0, ..., config.vocab_size]` (see
+        # `input_ids` docstring) Tokens with indices set to `-100` are ignored
+        # (masked), the loss is only computed for the tokens with labels in `[0,
+        # ..., config.vocab_size]`
+        #
+        # Prediction scores are only computed for masked tokens and the (bs,
+        # seqlen) dimensions are flattened
+        if (input_ids is not None) == (inputs_embeds is not None):
+            raise ValueError('Must specify either input_ids or input_embeds!')
+
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
+
+        outputs = self.bert(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+        prediction_scores = self.cls(sequence_output)
+
+        loss = None
+        if labels is not None:
+            # Compute loss
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
+
+        if not return_dict:
+            output = (prediction_scores,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return MaskedLMOutput(
+            loss=loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids: torch.Tensor, attention_mask: torch.Tensor, **model_kwargs
+    ):
+        input_shape = input_ids.shape
+        effective_batch_size = input_shape[0]
+
+        #  add a dummy token
+        if self.config.pad_token_id is None:
+            raise ValueError('The PAD token should be defined for generation')
+
+        attention_mask = torch.cat(
+            [attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))],
+            dim=-1,
+        )
+        dummy_token = torch.full(
+            (effective_batch_size, 1),
+            self.config.pad_token_id,
+            dtype=torch.long,
+            device=input_ids.device,
+        )
+        input_ids = torch.cat([input_ids, dummy_token], dim=1)
+
+        return {'input_ids': input_ids, 'attention_mask': attention_mask}
+
+
+
+class JBertForSequenceClassification(BertPreTrainedModel):
+    """Bert Model transformer with a sequence classification/regression head.
+
+    This head is just a linear layer on top of the pooled output. Used for,
+    e.g., GLUE tasks.
+    """
+
+    config_class = JBertConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.config = config
+
+        self.bert = JBertModel(config)
+        classifier_dropout = (
+            config.classifier_dropout
+            if config.classifier_dropout is not None
+            else config.hidden_dropout_prob
+        )
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.classifier = nn.Linear(config.hidden_size, 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,
+    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
+        # 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,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+
+        loss = None
+        if labels is not None:
+            # Compute loss
+            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=None,
+            attentions=None,
+        )

special_tokens_map (1).json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config (1).json

@@ -0,0 +1,15 @@
+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 8192,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}