v

layoutlmft/models/layoutlmv2/configuration_layoutlmv2.py

+# coding=utf-8
+from transformers.models.layoutlm.configuration_layoutlm import LayoutLMConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json",
+    "layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json",
+}
+
+
+class LayoutLMv2Config(LayoutLMConfig):
+    model_type = "layoutlmv2"
+
+    def __init__(
+        self,
+        vocab_size=30522,
+        hidden_size=768,
+        num_hidden_layers=12,
+        num_attention_heads=12,
+        intermediate_size=3072,
+        hidden_act="gelu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=2,
+        initializer_range=0.02,
+        layer_norm_eps=1e-12,
+        pad_token_id=0,
+        gradient_checkpointing=False,
+        max_2d_position_embeddings=1024,
+        max_rel_pos=128,
+        rel_pos_bins=32,
+        fast_qkv=True,
+        max_rel_2d_pos=256,
+        rel_2d_pos_bins=64,
+        convert_sync_batchnorm=True,
+        image_feature_pool_shape=[7, 7, 256],
+        coordinate_size=128,
+        shape_size=128,
+        has_relative_attention_bias=True,
+        has_spatial_attention_bias=True,
+        has_visual_segment_embedding=False,
+        **kwargs
+    ):
+        super().__init__(
+            vocab_size=vocab_size,
+            hidden_size=hidden_size,
+            num_hidden_layers=num_hidden_layers,
+            num_attention_heads=num_attention_heads,
+            intermediate_size=intermediate_size,
+            hidden_act=hidden_act,
+            hidden_dropout_prob=hidden_dropout_prob,
+            attention_probs_dropout_prob=attention_probs_dropout_prob,
+            max_position_embeddings=max_position_embeddings,
+            type_vocab_size=type_vocab_size,
+            initializer_range=initializer_range,
+            layer_norm_eps=layer_norm_eps,
+            pad_token_id=pad_token_id,
+            gradient_checkpointing=gradient_checkpointing,
+            **kwargs,
+        )
+        self.max_2d_position_embeddings = max_2d_position_embeddings
+        self.max_rel_pos = max_rel_pos
+        self.rel_pos_bins = rel_pos_bins
+        self.fast_qkv = fast_qkv
+        self.max_rel_2d_pos = max_rel_2d_pos
+        self.rel_2d_pos_bins = rel_2d_pos_bins
+        self.convert_sync_batchnorm = convert_sync_batchnorm
+        self.image_feature_pool_shape = image_feature_pool_shape
+        self.coordinate_size = coordinate_size
+        self.shape_size = shape_size
+        self.has_relative_attention_bias = has_relative_attention_bias
+        self.has_spatial_attention_bias = has_spatial_attention_bias
+        self.has_visual_segment_embedding = has_visual_segment_embedding

layoutlmft/models/layoutlmv2/detectron2_config.py

+# -*- coding: utf-8 -*-
+def add_layoutlmv2_config(cfg):
+    _C = cfg
+    # -----------------------------------------------------------------------------
+    # Config definition
+    # -----------------------------------------------------------------------------
+    _C.MODEL.MASK_ON = True
+
+    # When using pre-trained models in Detectron1 or any MSRA models,
+    # std has been absorbed into its conv1 weights, so the std needs to be set 1.
+    # Otherwise, you can use [57.375, 57.120, 58.395] (ImageNet std)
+    _C.MODEL.PIXEL_STD = [57.375, 57.120, 58.395]
+
+    # ---------------------------------------------------------------------------- #
+    # Backbone options
+    # ---------------------------------------------------------------------------- #
+    _C.MODEL.BACKBONE.NAME = "build_resnet_fpn_backbone"
+
+    # ---------------------------------------------------------------------------- #
+    # FPN options
+    # ---------------------------------------------------------------------------- #
+    # Names of the input feature maps to be used by FPN
+    # They must have contiguous power of 2 strides
+    # e.g., ["res2", "res3", "res4", "res5"]
+    _C.MODEL.FPN.IN_FEATURES = ["res2", "res3", "res4", "res5"]
+
+    # ---------------------------------------------------------------------------- #
+    # Anchor generator options
+    # ---------------------------------------------------------------------------- #
+    # Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input.
+    # Format: list[list[float]]. SIZES[i] specifies the list of sizes
+    # to use for IN_FEATURES[i]; len(SIZES) == len(IN_FEATURES) must be true,
+    # or len(SIZES) == 1 is true and size list SIZES[0] is used for all
+    # IN_FEATURES.
+    _C.MODEL.ANCHOR_GENERATOR.SIZES = [[32], [64], [128], [256], [512]]
+
+    # ---------------------------------------------------------------------------- #
+    # RPN options
+    # ---------------------------------------------------------------------------- #
+    # Names of the input feature maps to be used by RPN
+    # e.g., ["p2", "p3", "p4", "p5", "p6"] for FPN
+    _C.MODEL.RPN.IN_FEATURES = ["p2", "p3", "p4", "p5", "p6"]
+    # Number of top scoring RPN proposals to keep before applying NMS
+    # When FPN is used, this is *per FPN level* (not total)
+    _C.MODEL.RPN.PRE_NMS_TOPK_TRAIN = 2000
+    _C.MODEL.RPN.PRE_NMS_TOPK_TEST = 1000
+    # Number of top scoring RPN proposals to keep after applying NMS
+    # When FPN is used, this limit is applied per level and then again to the union
+    # of proposals from all levels
+    # NOTE: When FPN is used, the meaning of this config is different from Detectron1.
+    # It means per-batch topk in Detectron1, but per-image topk here.
+    # See the "find_top_rpn_proposals" function for details.
+    _C.MODEL.RPN.POST_NMS_TOPK_TRAIN = 1000
+    _C.MODEL.RPN.POST_NMS_TOPK_TEST = 1000
+
+    # ---------------------------------------------------------------------------- #
+    # ROI HEADS options
+    # ---------------------------------------------------------------------------- #
+    _C.MODEL.ROI_HEADS.NAME = "StandardROIHeads"
+    # Number of foreground classes
+    _C.MODEL.ROI_HEADS.NUM_CLASSES = 5
+    # Names of the input feature maps to be used by ROI heads
+    # Currently all heads (box, mask, ...) use the same input feature map list
+    # e.g., ["p2", "p3", "p4", "p5"] is commonly used for FPN
+    _C.MODEL.ROI_HEADS.IN_FEATURES = ["p2", "p3", "p4", "p5"]
+
+    # ---------------------------------------------------------------------------- #
+    # Box Head
+    # ---------------------------------------------------------------------------- #
+    # C4 don't use head name option
+    # Options for non-C4 models: FastRCNNConvFCHead,
+    _C.MODEL.ROI_BOX_HEAD.NAME = "FastRCNNConvFCHead"
+    _C.MODEL.ROI_BOX_HEAD.NUM_FC = 2
+    _C.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION = 14
+
+    # ---------------------------------------------------------------------------- #
+    # Mask Head
+    # ---------------------------------------------------------------------------- #
+    _C.MODEL.ROI_MASK_HEAD.NAME = "MaskRCNNConvUpsampleHead"
+    _C.MODEL.ROI_MASK_HEAD.NUM_CONV = 4  # The number of convs in the mask head
+    _C.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION = 7
+
+    # ---------------------------------------------------------------------------- #
+    # ResNe[X]t options (ResNets = {ResNet, ResNeXt}
+    # Note that parts of a resnet may be used for both the backbone and the head
+    # These options apply to both
+    # ---------------------------------------------------------------------------- #
+    _C.MODEL.RESNETS.DEPTH = 101
+    _C.MODEL.RESNETS.SIZES = [[32], [64], [128], [256], [512]]
+    _C.MODEL.RESNETS.ASPECT_RATIOS = [[0.5, 1.0, 2.0]]
+    _C.MODEL.RESNETS.OUT_FEATURES = ["res2", "res3", "res4", "res5"]  # res4 for C4 backbone, res2..5 for FPN backbone
+
+    # Number of groups to use; 1 ==> ResNet; > 1 ==> ResNeXt
+    _C.MODEL.RESNETS.NUM_GROUPS = 32
+
+    # Baseline width of each group.
+    # Scaling this parameters will scale the width of all bottleneck layers.
+    _C.MODEL.RESNETS.WIDTH_PER_GROUP = 8
+
+    # Place the stride 2 conv on the 1x1 filter
+    # Use True only for the original MSRA ResNet; use False for C2 and Torch models
+    _C.MODEL.RESNETS.STRIDE_IN_1X1 = False

layoutlmft/models/layoutlmv2/modeling_layoutlmv2.py

+# coding=utf-8
+import math
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+from libs.model.extractor import RoIPool
+
+import detectron2
+from detectron2.modeling import META_ARCH_REGISTRY
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPoolingAndCrossAttentions,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
+from transformers.models.layoutlm.modeling_layoutlm import LayoutLMIntermediate as LayoutLMv2Intermediate
+from transformers.models.layoutlm.modeling_layoutlm import LayoutLMOutput as LayoutLMv2Output
+from transformers.models.layoutlm.modeling_layoutlm import LayoutLMPooler as LayoutLMv2Pooler
+from transformers.models.layoutlm.modeling_layoutlm import LayoutLMSelfOutput as LayoutLMv2SelfOutput
+from transformers.utils import logging
+
+from ...modules.decoders.re import REDecoder
+from ...utils import ReOutput
+from .configuration_layoutlmv2 import LayoutLMv2Config
+from .detectron2_config import add_layoutlmv2_config
+
+
+logger = logging.get_logger(__name__)
+
+LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "layoutlmv2-base-uncased",
+    "layoutlmv2-large-uncased",
+]
+
+
+LayoutLMv2LayerNorm = torch.nn.LayerNorm
+
+
+class LayoutLMv2Embeddings(nn.Module):
+    """Construct the embeddings from word, position and token_type embeddings."""
+
+    def __init__(self, config):
+        super(LayoutLMv2Embeddings, self).__init__()
+        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
+        self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
+
+        self.x_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
+        self.y_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.coordinate_size)
+        self.h_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
+        self.w_position_embeddings = nn.Embedding(config.max_2d_position_embeddings, config.shape_size)
+        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
+
+        self.LayerNorm = LayoutLMv2LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+
+    def _cal_spatial_position_embeddings(self, bbox):
+        try:
+            left_position_embeddings = self.x_position_embeddings(bbox[:, :, 0])
+            upper_position_embeddings = self.y_position_embeddings(bbox[:, :, 1])
+            right_position_embeddings = self.x_position_embeddings(bbox[:, :, 2])
+            lower_position_embeddings = self.y_position_embeddings(bbox[:, :, 3])
+        except IndexError as e:
+            raise IndexError("The :obj:`bbox`coordinate values should be within 0-1000 range.") from e
+
+        h_position_embeddings = self.h_position_embeddings(bbox[:, :, 3] - bbox[:, :, 1])
+        w_position_embeddings = self.w_position_embeddings(bbox[:, :, 2] - bbox[:, :, 0])
+
+        spatial_position_embeddings = torch.cat(
+            [
+                left_position_embeddings,
+                upper_position_embeddings,
+                right_position_embeddings,
+                lower_position_embeddings,
+                h_position_embeddings,
+                w_position_embeddings,
+            ],
+            dim=-1,
+        )
+        return spatial_position_embeddings
+
+
+class LayoutLMv2SelfAttention(nn.Module):
+    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.fast_qkv = config.fast_qkv
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+
+        self.has_relative_attention_bias = config.has_relative_attention_bias
+        self.has_spatial_attention_bias = config.has_spatial_attention_bias
+
+        if config.fast_qkv:
+            self.qkv_linear = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=False)
+            self.q_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size))
+            self.v_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size))
+        else:
+            self.query = nn.Linear(config.hidden_size, self.all_head_size)
+            self.key = nn.Linear(config.hidden_size, self.all_head_size)
+            self.value = nn.Linear(config.hidden_size, self.all_head_size)
+
+        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
+
+    def transpose_for_scores(self, x):
+        new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
+        x = x.view(*new_x_shape)
+        return x.permute(0, 2, 1, 3)
+
+    def compute_qkv(self, hidden_states):
+        if self.fast_qkv:
+            qkv = self.qkv_linear(hidden_states)
+            q, k, v = torch.chunk(qkv, 3, dim=-1)
+            if q.ndimension() == self.q_bias.ndimension():
+                q = q + self.q_bias
+                v = v + self.v_bias
+            else:
+                _sz = (1,) * (q.ndimension() - 1) + (-1,)
+                q = q + self.q_bias.view(*_sz)
+                v = v + self.v_bias.view(*_sz)
+        else:
+            q = self.query(hidden_states)
+            k = self.key(hidden_states)
+            v = self.value(hidden_states)
+        return q, k, v
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+        rel_pos=None,
+        rel_2d_pos=None,
+    ):
+        q, k, v = self.compute_qkv(hidden_states)
+
+        # (B, L, H*D) -> (B, H, L, D)
+        query_layer = self.transpose_for_scores(q)
+        key_layer = self.transpose_for_scores(k)
+        value_layer = self.transpose_for_scores(v)
+
+        query_layer = query_layer / math.sqrt(self.attention_head_size)
+        # [BSZ, NAT, L, L]
+        attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+        if self.has_relative_attention_bias:
+            attention_scores += rel_pos
+        if self.has_spatial_attention_bias:
+            attention_scores += rel_2d_pos
+        attention_scores = attention_scores.float().masked_fill_(attention_mask.to(torch.bool), float(-1e8))
+        attention_probs = F.softmax(attention_scores, dim=-1, dtype=torch.float32).type_as(value_layer)
+        # This is actually dropping out entire tokens to attend to, which might
+        # seem a bit unusual, but is taken from the original Transformer paper.
+        attention_probs = self.dropout(attention_probs)
+        context_layer = torch.matmul(attention_probs, value_layer)
+        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.view(*new_context_layer_shape)
+
+        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
+        return outputs
+
+
+class LayoutLMv2Attention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.self = LayoutLMv2SelfAttention(config)
+        self.output = LayoutLMv2SelfOutput(config)
+        self.pruned_heads = set()
+
+    def prune_heads(self, heads):
+        if len(heads) == 0:
+            return
+        heads, index = find_pruneable_heads_and_indices(
+            heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads
+        )
+
+        # Prune linear layers
+        self.self.query = prune_linear_layer(self.self.query, index)
+        self.self.key = prune_linear_layer(self.self.key, index)
+        self.self.value = prune_linear_layer(self.self.value, index)
+        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
+
+        # Update hyper params and store pruned heads
+        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
+        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
+        self.pruned_heads = self.pruned_heads.union(heads)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+        rel_pos=None,
+        rel_2d_pos=None,
+    ):
+        self_outputs = self.self(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            past_key_value,
+            output_attentions,
+            rel_pos=rel_pos,
+            rel_2d_pos=rel_2d_pos,
+        )
+        attention_output = self.output(self_outputs[0], hidden_states)
+        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
+        return outputs
+
+
+class LayoutLMv2Layer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.chunk_size_feed_forward = config.chunk_size_feed_forward
+        self.seq_len_dim = 1
+        self.attention = LayoutLMv2Attention(config)
+        self.is_decoder = config.is_decoder
+        self.add_cross_attention = config.add_cross_attention
+        if self.add_cross_attention:
+            assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
+            self.crossattention = LayoutLMv2Attention(config)
+        self.intermediate = LayoutLMv2Intermediate(config)
+        self.output = LayoutLMv2Output(config)
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_value=None,
+        output_attentions=False,
+        rel_pos=None,
+        rel_2d_pos=None,
+    ):
+        # decoder uni-directional self-attention cached key/values tuple is at positions 1,2
+        self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None
+        self_attention_outputs = self.attention(
+            hidden_states,
+            attention_mask,
+            head_mask,
+            output_attentions=output_attentions,
+            past_key_value=self_attn_past_key_value,
+            rel_pos=rel_pos,
+            rel_2d_pos=rel_2d_pos,
+        )
+        attention_output = self_attention_outputs[0]
+
+        # if decoder, the last output is tuple of self-attn cache
+        if self.is_decoder:
+            outputs = self_attention_outputs[1:-1]
+            present_key_value = self_attention_outputs[-1]
+        else:
+            outputs = self_attention_outputs[1:]  # add self attentions if we output attention weights
+
+        cross_attn_present_key_value = None
+        if self.is_decoder and encoder_hidden_states is not None:
+            assert hasattr(
+                self, "crossattention"
+            ), f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers by setting `config.add_cross_attention=True`"
+
+            # cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple
+            cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None
+            cross_attention_outputs = self.crossattention(
+                attention_output,
+                attention_mask,
+                head_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                cross_attn_past_key_value,
+                output_attentions,
+            )
+            attention_output = cross_attention_outputs[0]
+            outputs = outputs + cross_attention_outputs[1:-1]  # add cross attentions if we output attention weights
+
+            # add cross-attn cache to positions 3,4 of present_key_value tuple
+            cross_attn_present_key_value = cross_attention_outputs[-1]
+            present_key_value = present_key_value + cross_attn_present_key_value
+
+        layer_output = apply_chunking_to_forward(
+            self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output
+        )
+        outputs = (layer_output,) + outputs
+
+        # if decoder, return the attn key/values as the last output
+        if self.is_decoder:
+            outputs = outputs + (present_key_value,)
+
+        return outputs
+
+    def feed_forward_chunk(self, attention_output):
+        intermediate_output = self.intermediate(attention_output)
+        layer_output = self.output(intermediate_output, attention_output)
+        return layer_output
+
+
+def relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+    ret = 0
+    if bidirectional:
+        num_buckets //= 2
+        ret += (relative_position > 0).long() * num_buckets
+        n = torch.abs(relative_position)
+    else:
+        n = torch.max(-relative_position, torch.zeros_like(relative_position))
+    # now n is in the range [0, inf)
+
+    # half of the buckets are for exact increments in positions
+    max_exact = num_buckets // 2
+    is_small = n < max_exact
+
+    # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+    val_if_large = max_exact + (
+        torch.log(n.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)
+    ).to(torch.long)
+    val_if_large = torch.min(val_if_large, torch.full_like(val_if_large, num_buckets - 1))
+
+    ret += torch.where(is_small, n, val_if_large)
+    return ret
+
+
+class LayoutLMv2Encoder(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([LayoutLMv2Layer(config) for _ in range(config.num_hidden_layers)])
+
+        self.has_relative_attention_bias = config.has_relative_attention_bias
+        self.has_spatial_attention_bias = config.has_spatial_attention_bias
+
+        if self.has_relative_attention_bias:
+            self.rel_pos_bins = config.rel_pos_bins
+            self.max_rel_pos = config.max_rel_pos
+            self.rel_pos_onehot_size = config.rel_pos_bins
+            self.rel_pos_bias = nn.Linear(self.rel_pos_onehot_size, config.num_attention_heads, bias=False)
+
+        if self.has_spatial_attention_bias:
+            self.max_rel_2d_pos = config.max_rel_2d_pos
+            self.rel_2d_pos_bins = config.rel_2d_pos_bins
+            self.rel_2d_pos_onehot_size = config.rel_2d_pos_bins
+            self.rel_pos_x_bias = nn.Linear(self.rel_2d_pos_onehot_size, config.num_attention_heads, bias=False)
+            self.rel_pos_y_bias = nn.Linear(self.rel_2d_pos_onehot_size, config.num_attention_heads, bias=False)
+
+    def _cal_1d_pos_emb(self, hidden_states, position_ids):
+        rel_pos_mat = position_ids.unsqueeze(-2) - position_ids.unsqueeze(-1)
+        rel_pos = relative_position_bucket(
+            rel_pos_mat,
+            num_buckets=self.rel_pos_bins,
+            max_distance=self.max_rel_pos,
+        )
+        rel_pos = F.one_hot(rel_pos, num_classes=self.rel_pos_onehot_size).type_as(hidden_states)
+        rel_pos = self.rel_pos_bias(rel_pos).permute(0, 3, 1, 2)
+        rel_pos = rel_pos.contiguous()
+        return rel_pos
+
+    def _cal_2d_pos_emb(self, hidden_states, bbox):
+        position_coord_x = bbox[:, :, 0]
+        position_coord_y = bbox[:, :, 3]
+        rel_pos_x_2d_mat = position_coord_x.unsqueeze(-2) - position_coord_x.unsqueeze(-1)
+        rel_pos_y_2d_mat = position_coord_y.unsqueeze(-2) - position_coord_y.unsqueeze(-1)
+        rel_pos_x = relative_position_bucket(
+            rel_pos_x_2d_mat,
+            num_buckets=self.rel_2d_pos_bins,
+            max_distance=self.max_rel_2d_pos,
+        )
+        rel_pos_y = relative_position_bucket(
+            rel_pos_y_2d_mat,
+            num_buckets=self.rel_2d_pos_bins,
+            max_distance=self.max_rel_2d_pos,
+        )
+        rel_pos_x = F.one_hot(rel_pos_x, num_classes=self.rel_2d_pos_onehot_size).type_as(hidden_states)
+        rel_pos_y = F.one_hot(rel_pos_y, num_classes=self.rel_2d_pos_onehot_size).type_as(hidden_states)
+        rel_pos_x = self.rel_pos_x_bias(rel_pos_x).permute(0, 3, 1, 2)
+        rel_pos_y = self.rel_pos_y_bias(rel_pos_y).permute(0, 3, 1, 2)
+        rel_pos_x = rel_pos_x.contiguous()
+        rel_pos_y = rel_pos_y.contiguous()
+        rel_2d_pos = rel_pos_x + rel_pos_y
+        return rel_2d_pos
+
+    def forward(
+        self,
+        hidden_states,
+        attention_mask=None,
+        head_mask=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=False,
+        output_hidden_states=False,
+        return_dict=True,
+        bbox=None,
+        position_ids=None,
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attentions = () if output_attentions else None
+        all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None
+
+        next_decoder_cache = () if use_cache else None
+
+        rel_pos = self._cal_1d_pos_emb(hidden_states, position_ids) if self.has_relative_attention_bias else None
+        rel_2d_pos = self._cal_2d_pos_emb(hidden_states, bbox) if self.has_spatial_attention_bias else None
+
+        for i, layer_module in enumerate(self.layer):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer_head_mask = head_mask[i] if head_mask is not None else None
+            past_key_value = past_key_values[i] if past_key_values is not None else None
+
+            if getattr(self.config, "gradient_checkpointing", False) and self.training:
+
+                if use_cache:
+                    logger.warn(
+                        "`use_cache=True` is incompatible with `config.gradient_checkpointing=True`. Setting "
+                        "`use_cache=False`..."
+                    )
+                    use_cache = False
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        return module(*inputs, past_key_value, output_attentions)
+
+                    return custom_forward
+
+                layer_outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer_module),
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    rel_pos=rel_pos,
+                    rel_2d_pos=rel_2d_pos,
+                )
+            else:
+                layer_outputs = layer_module(
+                    hidden_states,
+                    attention_mask,
+                    layer_head_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    past_key_value,
+                    output_attentions,
+                    rel_pos=rel_pos,
+                    rel_2d_pos=rel_2d_pos,
+                )
+
+            hidden_states = layer_outputs[0]
+            if use_cache:
+                next_decoder_cache += (layer_outputs[-1],)
+            if output_attentions:
+                all_self_attentions = all_self_attentions + (layer_outputs[1],)
+                if self.config.add_cross_attention:
+                    all_cross_attentions = all_cross_attentions + (layer_outputs[2],)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    next_decoder_cache,
+                    all_hidden_states,
+                    all_self_attentions,
+                    all_cross_attentions,
+                ]
+                if v is not None
+            )
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=next_decoder_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attentions,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+class LayoutLMv2PreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = LayoutLMv2Config
+    pretrained_model_archive_map = LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST
+    base_model_prefix = "layoutlmv2"
+    _keys_to_ignore_on_load_missing = [r"position_ids"]
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, LayoutLMv2LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+
+def my_convert_sync_batchnorm(module, process_group=None):
+    # same as `nn.modules.SyncBatchNorm.convert_sync_batchnorm` but allowing converting from `detectron2.layers.FrozenBatchNorm2d`
+    if isinstance(module, torch.nn.modules.batchnorm._BatchNorm):
+        return nn.modules.SyncBatchNorm.convert_sync_batchnorm(module, process_group)
+    module_output = module
+    if isinstance(module, detectron2.layers.FrozenBatchNorm2d):
+        module_output = torch.nn.SyncBatchNorm(
+            num_features=module.num_features,
+            eps=module.eps,
+            affine=True,
+            track_running_stats=True,
+            process_group=process_group,
+        )
+        module_output.weight = torch.nn.Parameter(module.weight)
+        module_output.bias = torch.nn.Parameter(module.bias)
+        module_output.running_mean = module.running_mean
+        module_output.running_var = module.running_var
+        module_output.num_batches_tracked = torch.tensor(0, dtype=torch.long, device=module.running_mean.device)
+    for name, child in module.named_children():
+        module_output.add_module(name, my_convert_sync_batchnorm(child, process_group))
+    del module
+    return module_output
+
+
+class VisualBackbone(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.cfg = detectron2.config.get_cfg()
+        add_layoutlmv2_config(self.cfg)
+        meta_arch = self.cfg.MODEL.META_ARCHITECTURE
+        model = META_ARCH_REGISTRY.get(meta_arch)(self.cfg)
+        assert isinstance(model.backbone, detectron2.modeling.backbone.FPN)
+        self.backbone = model.backbone
+        if (
+            config.convert_sync_batchnorm
+            and torch.distributed.is_available()
+            and torch.distributed.is_initialized()
+            and torch.distributed.get_rank() > -1
+        ):
+            self_rank = torch.distributed.get_rank()
+            node_size = torch.cuda.device_count()
+            world_size = torch.distributed.get_world_size()
+            assert world_size % node_size == 0
+
+            node_global_ranks = [
+                list(range(i * node_size, (i + 1) * node_size)) for i in range(world_size // node_size)
+            ]
+            sync_bn_groups = [
+                torch.distributed.new_group(ranks=node_global_ranks[i]) for i in range(world_size // node_size)
+            ]
+            node_rank = self_rank // node_size
+            assert self_rank in node_global_ranks[node_rank]
+
+            self.backbone = my_convert_sync_batchnorm(self.backbone, process_group=sync_bn_groups[node_rank])
+
+        assert len(self.cfg.MODEL.PIXEL_MEAN) == len(self.cfg.MODEL.PIXEL_STD)
+        num_channels = len(self.cfg.MODEL.PIXEL_MEAN)
+        self.register_buffer(
+            "pixel_mean",
+            torch.Tensor(self.cfg.MODEL.PIXEL_MEAN).view(num_channels, 1, 1),
+        )
+        self.register_buffer("pixel_std", torch.Tensor(self.cfg.MODEL.PIXEL_STD).view(num_channels, 1, 1))
+        self.out_feature_key = "p2"
+        # if torch.is_deterministic():
+        #     logger.warning("using `AvgPool2d` instead of `AdaptiveAvgPool2d`")
+        #     input_shape = (224, 224)
+        #     backbone_stride = self.backbone.output_shape()[self.out_feature_key].stride
+        #     self.pool = nn.AvgPool2d(
+        #         (
+        #             math.ceil(math.ceil(input_shape[0] / backbone_stride) / config.image_feature_pool_shape[0]),
+        #             math.ceil(math.ceil(input_shape[1] / backbone_stride) / config.image_feature_pool_shape[1]),
+        #         )
+        #     )
+        # else:
+        #     self.pool = nn.AdaptiveAvgPool2d(config.image_feature_pool_shape[:2])
+        self.pool = RoIPool(config.image_feature_pool_shape[:2])
+        if len(config.image_feature_pool_shape) == 2:
+            config.image_feature_pool_shape.append(self.backbone.output_shape()[self.out_feature_key].channels)
+        assert self.backbone.output_shape()[self.out_feature_key].channels == config.image_feature_pool_shape[2]
+
+    def forward(self, images):
+        images_input = (images.tensor - self.pixel_mean) / self.pixel_std
+        features = self.backbone(images_input)
+        features = features[self.out_feature_key]
+        # features = self.pool(features).flatten(start_dim=2).transpose(1, 2).contiguous()
+        features = self.pool(features) # notice that self.pool has been modified
+        return features
+
+
+class LayoutLMv2Model(LayoutLMv2PreTrainedModel):
+    def __init__(self, config):
+        super(LayoutLMv2Model, self).__init__(config)
+        self.config = config
+        self.has_visual_segment_embedding = config.has_visual_segment_embedding
+        self.embeddings = LayoutLMv2Embeddings(config)
+
+        self.visual = VisualBackbone(config)
+        self.visual_proj = nn.Linear(config.image_feature_pool_shape[-1], config.hidden_size)
+        if self.has_visual_segment_embedding:
+            self.visual_segment_embedding = nn.Parameter(nn.Embedding(1, config.hidden_size).weight[0])
+        self.visual_LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.visual_dropout = nn.Dropout(config.hidden_dropout_prob)
+
+        self.encoder = LayoutLMv2Encoder(config)
+        self.pooler = LayoutLMv2Pooler(config)
+
+        self.init_weights()
+
+    def get_input_embeddings(self):
+        return self.embeddings.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embeddings.word_embeddings = value
+
+    def _prune_heads(self, heads_to_prune):
+        """
+        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
+        class PreTrainedModel
+        """
+        for layer, heads in heads_to_prune.items():
+            self.encoder.layer[layer].attention.prune_heads(heads)
+
+    def _calc_text_embeddings(self, input_ids, bbox, position_ids, token_type_ids):
+        seq_length = input_ids.size(1)
+        if position_ids is None:
+            position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+        if token_type_ids is None:
+            token_type_ids = torch.zeros_like(input_ids)
+
+        words_embeddings = self.embeddings.word_embeddings(input_ids)
+        position_embeddings = self.embeddings.position_embeddings(position_ids)
+        spatial_position_embeddings = self.embeddings._cal_spatial_position_embeddings(bbox)
+        token_type_embeddings = self.embeddings.token_type_embeddings(token_type_ids)
+        embeddings = words_embeddings + position_embeddings + spatial_position_embeddings + token_type_embeddings
+        embeddings = self.embeddings.LayerNorm(embeddings)
+        embeddings = self.embeddings.dropout(embeddings)
+        return embeddings
+
+    def _calc_img_embeddings(self, image, bbox, position_ids):
+        visual_embeddings = self.visual_proj(self.visual(image))
+        position_embeddings = self.embeddings.position_embeddings(position_ids)
+        spatial_position_embeddings = self.embeddings._cal_spatial_position_embeddings(bbox)
+        embeddings = visual_embeddings + position_embeddings + spatial_position_embeddings
+        if self.has_visual_segment_embedding:
+            embeddings += self.visual_segment_embedding
+        embeddings = self.visual_LayerNorm(embeddings)
+        embeddings = self.visual_dropout(embeddings)
+        return embeddings
+
+    def forward(
+        self,
+        input_ids=None,
+        bbox=None,
+        image=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        visual_shape = list(input_shape)
+        visual_shape[1] = self.config.image_feature_pool_shape[0] * self.config.image_feature_pool_shape[1]
+        visual_shape = torch.Size(visual_shape)
+        final_shape = list(input_shape)
+        final_shape[1] += visual_shape[1]
+        final_shape = torch.Size(final_shape)
+
+        visual_bbox_x = (
+            torch.arange(
+                0,
+                1000 * (self.config.image_feature_pool_shape[1] + 1),
+                1000,
+                device=device,
+                dtype=bbox.dtype,
+            )
+            // self.config.image_feature_pool_shape[1]
+        )
+        visual_bbox_y = (
+            torch.arange(
+                0,
+                1000 * (self.config.image_feature_pool_shape[0] + 1),
+                1000,
+                device=device,
+                dtype=bbox.dtype,
+            )
+            // self.config.image_feature_pool_shape[0]
+        )
+        visual_bbox = torch.stack(
+            [
+                visual_bbox_x[:-1].repeat(self.config.image_feature_pool_shape[0], 1),
+                visual_bbox_y[:-1].repeat(self.config.image_feature_pool_shape[1], 1).transpose(0, 1),
+                visual_bbox_x[1:].repeat(self.config.image_feature_pool_shape[0], 1),
+                visual_bbox_y[1:].repeat(self.config.image_feature_pool_shape[1], 1).transpose(0, 1),
+            ],
+            dim=-1,
+        ).view(-1, bbox.size(-1))
+        visual_bbox = visual_bbox.repeat(final_shape[0], 1, 1)
+        final_bbox = torch.cat([bbox, visual_bbox], dim=1)
+
+        if attention_mask is None:
+            attention_mask = torch.ones(input_shape, device=device)
+
+        visual_attention_mask = torch.ones(visual_shape, device=device)
+        final_attention_mask = torch.cat([attention_mask, visual_attention_mask], dim=1)
+
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+        if position_ids is None:
+            seq_length = input_shape[1]
+            position_ids = self.embeddings.position_ids[:, :seq_length]
+            position_ids = position_ids.expand_as(input_ids)
+
+        visual_position_ids = torch.arange(0, visual_shape[1], dtype=torch.long, device=input_ids.device).repeat(
+            input_shape[0], 1
+        )
+        final_position_ids = torch.cat([position_ids, visual_position_ids], dim=1)
+
+        if bbox is None:
+            bbox = torch.zeros(tuple(list(input_shape) + [4]), dtype=torch.long, device=device)
+
+        text_layout_emb = self._calc_text_embeddings(
+            input_ids=input_ids,
+            bbox=bbox,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+        )
+
+        visual_emb = self._calc_img_embeddings(
+            image=image,
+            bbox=visual_bbox,
+            position_ids=visual_position_ids,
+        )
+
+        final_emb = torch.cat([text_layout_emb, visual_emb], dim=1)
+
+        extended_attention_mask = final_attention_mask.unsqueeze(1).unsqueeze(2)
+
+        extended_attention_mask = extended_attention_mask.to(dtype=self.dtype)
+        extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
+
+        if head_mask is not None:
+            if head_mask.dim() == 1:
+                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
+                head_mask = head_mask.expand(self.config.num_hidden_layers, -1, -1, -1, -1)
+            elif head_mask.dim() == 2:
+                head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1)
+            head_mask = head_mask.to(dtype=next(self.parameters()).dtype)
+        else:
+            head_mask = [None] * self.config.num_hidden_layers
+
+        encoder_outputs = self.encoder(
+            final_emb,
+            extended_attention_mask,
+            bbox=final_bbox,
+            position_ids=final_position_ids,
+            head_mask=head_mask,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(sequence_output)
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+
+class LayoutLMv2ForTokenClassification(LayoutLMv2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.layoutlmv2 = LayoutLMv2Model(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
+
+        self.init_weights()
+
+    def get_input_embeddings(self):
+        return self.layoutlmv2.embeddings.word_embeddings
+
+    def forward(
+        self,
+        input_ids=None,
+        bbox=None,
+        image=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        labels=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.layoutlmv2(
+            input_ids=input_ids,
+            bbox=bbox,
+            image=image,
+            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,
+        )
+            
+        seq_length = input_ids.size(1)
+        sequence_output, image_output = outputs[0][:, :seq_length], outputs[0][:, seq_length:]
+        sequence_output = self.dropout(sequence_output)
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+
+            if attention_mask is not None:
+                active_loss = attention_mask.view(-1) == 1
+                active_logits = logits.view(-1, self.num_labels)[active_loss]
+                active_labels = labels.view(-1)[active_loss]
+                loss = loss_fct(active_logits, active_labels)
+            else:
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class LayoutLMv2ForRelationExtraction(LayoutLMv2PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.layoutlmv2 = LayoutLMv2Model(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.extractor = REDecoder(config)
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids,
+        bbox,
+        labels=None,
+        image=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        entities=None,
+        relations=None,
+    ):
+        outputs = self.layoutlmv2(
+            input_ids=input_ids,
+            bbox=bbox,
+            image=image,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+        )
+
+        seq_length = input_ids.size(1)
+        sequence_output, image_output = outputs[0][:, :seq_length], outputs[0][:, seq_length:]
+        sequence_output = self.dropout(sequence_output)
+        loss, pred_relations = self.extractor(sequence_output, entities, relations)
+
+        return ReOutput(
+            loss=loss,
+            entities=entities,
+            relations=relations,
+            pred_relations=pred_relations,
+            hidden_states=outputs[0],
+        )

layoutlmft/models/layoutlmv2/tokenization_layoutlmv2.py

+# coding=utf-8
+from transformers.models.layoutlm.tokenization_layoutlm import LayoutLMTokenizer
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "microsoft/layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/vocab.txt",
+        "microsoft/layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/vocab.txt",
+    }
+}
+
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "microsoft/layoutlmv2-base-uncased": 512,
+    "microsoft/layoutlmv2-large-uncased": 512,
+}
+
+
+PRETRAINED_INIT_CONFIGURATION = {
+    "microsoft/layoutlmv2-base-uncased": {"do_lower_case": True},
+    "microsoft/layoutlmv2-large-uncased": {"do_lower_case": True},
+}
+
+
+class LayoutLMv2Tokenizer(LayoutLMTokenizer):
+    r"""
+    Constructs a LayoutLMv2 tokenizer.
+
+    :class:`~transformers.LayoutLMv2Tokenizer is identical to :class:`~transformers.BertTokenizer` and runs end-to-end
+    tokenization: punctuation splitting + wordpiece.
+
+    Refer to superclass :class:`~transformers.BertTokenizer` for usage examples and documentation concerning
+    parameters.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+
+    def __init__(self, model_max_length=512, **kwargs):
+        super().__init__(model_max_length=model_max_length, **kwargs)

layoutlmft/models/layoutlmv2/tokenization_layoutlmv2_fast.py

+# coding=utf-8
+from transformers.models.layoutlm.tokenization_layoutlm_fast import LayoutLMTokenizerFast
+from transformers.utils import logging
+
+from .tokenization_layoutlmv2 import LayoutLMv2Tokenizer
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "microsoft/layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/vocab.txt",
+        "microsoft/layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/vocab.txt",
+    },
+    "tokenizer_file": {
+        "microsoft/layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/tokenizer.json",
+        "microsoft/layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/tokenizer.json",
+    },
+}
+
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "microsoft/layoutlmv2-base-uncased": 512,
+    "microsoft/layoutlmv2-large-uncased": 512,
+}
+
+
+PRETRAINED_INIT_CONFIGURATION = {
+    "microsoft/layoutlmv2-base-uncased": {"do_lower_case": True},
+    "microsoft/layoutlmv2-large-uncased": {"do_lower_case": True},
+}
+
+
+class LayoutLMv2TokenizerFast(LayoutLMTokenizerFast):
+    r"""
+    Constructs a "Fast" LayoutLMv2Tokenizer.
+
+    Refer to superclass :class:`~transformers.BertTokenizerFast` for usage examples and documentation concerning
+    parameters.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
+    slow_tokenizer_class = LayoutLMv2Tokenizer
+
+    def __init__(self, model_max_length=512, **kwargs):
+        super().__init__(model_max_length=model_max_length, **kwargs)

layoutlmft/models/layoutxlm/__init__.py

+from .configuration_layoutxlm import LayoutXLMConfig
+from .modeling_layoutxlm import LayoutXLMForRelationExtraction, LayoutXLMForTokenClassification, LayoutXLMModel
+from .tokenization_layoutxlm import LayoutXLMTokenizer
+from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast