diff --git "a/modeling_bert.py" "b/modeling_bert.py"
new file mode 100644--- /dev/null
+++ "b/modeling_bert.py"
@@ -0,0 +1,2357 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2023 Jina AI GmbH. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch BERT model."""
+
+
+import math
+import os
+import warnings
+from dataclasses import dataclass
+from typing import List, Optional, Tuple, Union
+import numpy as np
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import (
+ BaseModelOutputWithPastAndCrossAttentions,
+ BaseModelOutputWithPoolingAndCrossAttentions,
+ CausalLMOutputWithCrossAttentions,
+ MaskedLMOutput,
+ MultipleChoiceModelOutput,
+ NextSentencePredictorOutput,
+ QuestionAnsweringModelOutput,
+ SequenceClassifierOutput,
+ TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import (
+ apply_chunking_to_forward,
+ find_pruneable_heads_and_indices,
+ prune_linear_layer,
+)
+from transformers.utils import (
+ ModelOutput,
+ add_code_sample_docstrings,
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ logging,
+ replace_return_docstrings,
+)
+from .configuration_bert import JinaBertConfig
+
+# Torch implementation
+try:
+ from torch.nn.functional import scaled_dot_product_attention
+except ImportError:
+ scaled_dot_product_attention = None
+
+# This is used by encode but user may not have it installed
+try:
+ from tqdm.autonotebook import trange
+
+ has_tqdm = True
+except ImportError:
+ has_tqdm = False
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "bert-base-uncased"
+_CONFIG_FOR_DOC = "JinaBertConfig"
+
+# TokenClassification docstring
+_CHECKPOINT_FOR_TOKEN_CLASSIFICATION = (
+ "dbmdz/bert-large-cased-finetuned-conll03-english"
+)
+_TOKEN_CLASS_EXPECTED_OUTPUT = "['O', 'I-ORG', 'I-ORG', 'I-ORG', 'O', 'O', 'O', 'O', 'O', 'I-LOC', 'O', 'I-LOC', 'I-LOC'] "
+_TOKEN_CLASS_EXPECTED_LOSS = 0.01
+
+# QuestionAnswering docstring
+_CHECKPOINT_FOR_QA = "deepset/bert-base-cased-squad2"
+_QA_EXPECTED_OUTPUT = "'a nice puppet'"
+_QA_EXPECTED_LOSS = 7.41
+_QA_TARGET_START_INDEX = 14
+_QA_TARGET_END_INDEX = 15
+
+# SequenceClassification docstring
+_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION = "textattack/bert-base-uncased-yelp-polarity"
+_SEQ_CLASS_EXPECTED_OUTPUT = "'LABEL_1'"
+_SEQ_CLASS_EXPECTED_LOSS = 0.01
+
+
+def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
+ """Load tf checkpoints in a pytorch model."""
+ try:
+ import re
+
+ import numpy as np
+ import tensorflow as tf
+ except ImportError:
+ logger.error(
+ "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
+ "https://www.tensorflow.org/install/ for installation instructions."
+ )
+ raise
+ tf_path = os.path.abspath(tf_checkpoint_path)
+ logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
+ # Load weights from TF model
+ init_vars = tf.train.list_variables(tf_path)
+ names = []
+ arrays = []
+ for name, shape in init_vars:
+ logger.info(f"Loading TF weight {name} with shape {shape}")
+ array = tf.train.load_variable(tf_path, name)
+ names.append(name)
+ arrays.append(array)
+
+ for name, array in zip(names, arrays):
+ name = name.split("/")
+ # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
+ # which are not required for using pretrained model
+ if any(
+ n
+ in [
+ "adam_v",
+ "adam_m",
+ "AdamWeightDecayOptimizer",
+ "AdamWeightDecayOptimizer_1",
+ "global_step",
+ ]
+ for n in name
+ ):
+ logger.info(f"Skipping {'/'.join(name)}")
+ continue
+ pointer = model
+ for m_name in name:
+ if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
+ scope_names = re.split(r"_(\d+)", m_name)
+ else:
+ scope_names = [m_name]
+ if scope_names[0] == "kernel" or scope_names[0] == "gamma":
+ pointer = getattr(pointer, "weight")
+ elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
+ pointer = getattr(pointer, "bias")
+ elif scope_names[0] == "output_weights":
+ pointer = getattr(pointer, "weight")
+ elif scope_names[0] == "squad":
+ pointer = getattr(pointer, "classifier")
+ else:
+ try:
+ pointer = getattr(pointer, scope_names[0])
+ except AttributeError:
+ logger.info(f"Skipping {'/'.join(name)}")
+ continue
+ if len(scope_names) >= 2:
+ num = int(scope_names[1])
+ pointer = pointer[num]
+ if m_name[-11:] == "_embeddings":
+ pointer = getattr(pointer, "weight")
+ elif m_name == "kernel":
+ array = np.transpose(array)
+ try:
+ if pointer.shape != array.shape:
+ raise ValueError(
+ f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
+ )
+ except ValueError as e:
+ e.args += (pointer.shape, array.shape)
+ raise
+ logger.info(f"Initialize PyTorch weight {name}")
+ pointer.data = torch.from_numpy(array)
+ return model
+
+
+class JinaBertEmbeddings(nn.Module):
+ """Construct the embeddings from word, position and token_type embeddings."""
+
+ def __init__(self, config: JinaBertConfig):
+ super().__init__()
+ self.word_embeddings = nn.Embedding(
+ config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
+ )
+ if config.position_embedding_type != "alibi":
+ self.position_embeddings = nn.Embedding(
+ config.max_position_embeddings, config.hidden_size
+ )
+ 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)
+ # position_ids (1, len position emb) is contiguous in memory and exported when serialized
+ self.position_embedding_type = getattr(
+ config, "position_embedding_type", "absolute"
+ )
+ self.register_buffer(
+ "position_ids",
+ torch.arange(config.max_position_embeddings).expand((1, -1)),
+ persistent=False,
+ )
+ self.register_buffer(
+ "token_type_ids",
+ torch.zeros(self.position_ids.size(), 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:
+ input_shape = input_ids.size()
+ else:
+ input_shape = inputs_embeds.size()[:-1]
+
+ seq_length = input_shape[1]
+
+ if position_ids is None:
+ position_ids = self.position_ids[
+ :, past_key_values_length : seq_length + past_key_values_length
+ ]
+
+ # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs
+ # when its 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
+ else:
+ token_type_ids = torch.zeros(
+ input_shape, dtype=torch.long, device=self.position_ids.device
+ )
+
+ 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
+ if self.position_embedding_type == "absolute":
+ position_embeddings = self.position_embeddings(position_ids)
+ embeddings += position_embeddings
+ embeddings = self.LayerNorm(embeddings)
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+class JinaBertSelfAttention(nn.Module):
+ def __init__(self, config: JinaBertConfig, position_embedding_type=None):
+ 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.attn_implementation = config.attn_implementation
+ 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.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_p = config.attention_probs_dropout_prob
+ self.dropout = nn.Dropout(self.dropout_p)
+ self.position_embedding_type = position_embedding_type or getattr(
+ config, "position_embedding_type", "absolute"
+ )
+ if (
+ self.position_embedding_type == "relative_key"
+ or self.position_embedding_type == "relative_key_query"
+ ):
+ self.max_position_embeddings = config.max_position_embeddings
+ self.distance_embedding = nn.Embedding(
+ 2 * config.max_position_embeddings - 1, self.attention_head_size
+ )
+
+ self.is_decoder = config.is_decoder
+
+ def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
+ 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 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_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+ output_attentions: Optional[bool] = False,
+ bias: Optional[torch.FloatTensor] = None,
+ ) -> Tuple[torch.Tensor]:
+ mixed_query_layer = self.query(hidden_states)
+
+ # If this is instantiated as a cross-attention module, the keys
+ # and values come from an encoder; the attention mask needs to be
+ # such that the encoder's padding tokens are not attended to.
+ is_cross_attention = encoder_hidden_states is not None
+
+ if is_cross_attention and past_key_value is not None:
+ # reuse k,v, cross_attentions
+ key_layer = past_key_value[0]
+ value_layer = past_key_value[1]
+ attention_mask = encoder_attention_mask
+ elif is_cross_attention:
+ key_layer = self.transpose_for_scores(self.key(encoder_hidden_states))
+ value_layer = self.transpose_for_scores(self.value(encoder_hidden_states))
+ attention_mask = encoder_attention_mask
+ elif past_key_value is not None:
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
+ key_layer = torch.cat([past_key_value[0], key_layer], dim=2)
+ value_layer = torch.cat([past_key_value[1], value_layer], dim=2)
+ else:
+ key_layer = self.transpose_for_scores(self.key(hidden_states))
+ value_layer = self.transpose_for_scores(self.value(hidden_states))
+
+ query_layer = self.transpose_for_scores(mixed_query_layer)
+
+ use_cache = past_key_value is not None
+ if self.is_decoder:
+ # if cross_attention save Tuple(torch.Tensor, torch.Tensor) of all cross attention key/value_states.
+ # Further calls to cross_attention layer can then reuse all cross-attention
+ # key/value_states (first "if" case)
+ # if uni-directional self-attention (decoder) save Tuple(torch.Tensor, torch.Tensor) of
+ # all previous decoder key/value_states. Further calls to uni-directional self-attention
+ # can concat previous decoder key/value_states to current projected key/value_states (third "elif" case)
+ # if encoder bi-directional self-attention `past_key_value` is always `None`
+ past_key_value = (key_layer, value_layer)
+
+ if self.attn_implementation == 'torch' and scaled_dot_product_attention is not None:
+ b, _, s, _ = query_layer.shape
+ new_bias = attention_mask + bias
+ dropout_p = self.dropout_p if self.training else 0.0
+ attn = scaled_dot_product_attention(query_layer, key_layer, value_layer, new_bias, dropout_p=dropout_p)
+ attn = attn.permute(0, 2, 1, 3).contiguous()
+ return (attn.view(b, s, self.all_head_size),)
+
+ # Take the dot product between "query" and "key" to get the raw attention scores.
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
+
+ if (
+ self.position_embedding_type == "relative_key"
+ or self.position_embedding_type == "relative_key_query"
+ ):
+ query_length, key_length = query_layer.shape[2], key_layer.shape[2]
+ if use_cache:
+ position_ids_l = torch.tensor(
+ key_length - 1, dtype=torch.long, device=hidden_states.device
+ ).view(-1, 1)
+ else:
+ position_ids_l = torch.arange(
+ query_length, dtype=torch.long, device=hidden_states.device
+ ).view(-1, 1)
+ position_ids_r = torch.arange(
+ key_length, dtype=torch.long, device=hidden_states.device
+ ).view(1, -1)
+ distance = position_ids_l - position_ids_r
+
+ positional_embedding = self.distance_embedding(
+ distance + self.max_position_embeddings - 1
+ )
+ positional_embedding = positional_embedding.to(
+ dtype=query_layer.dtype
+ ) # fp16 compatibility
+
+ if self.position_embedding_type == "relative_key":
+ relative_position_scores = torch.einsum(
+ "bhld,lrd->bhlr", query_layer, positional_embedding
+ )
+ attention_scores = attention_scores + relative_position_scores
+ elif self.position_embedding_type == "relative_key_query":
+ relative_position_scores_query = torch.einsum(
+ "bhld,lrd->bhlr", query_layer, positional_embedding
+ )
+ relative_position_scores_key = torch.einsum(
+ "bhrd,lrd->bhlr", key_layer, positional_embedding
+ )
+ attention_scores = (
+ attention_scores
+ + relative_position_scores_query
+ + relative_position_scores_key
+ )
+
+ attention_scores = attention_scores / math.sqrt(self.attention_head_size)
+ if attention_mask is not None:
+ # Apply the attention mask is (precomputed for all layers in BertModel forward() function)
+ attention_scores = attention_scores + attention_mask
+
+ # Normalize the attention scores to probabilities.
+ attention_probs = nn.functional.softmax(attention_scores + bias, dim=-1)
+
+ # 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)
+
+ # Mask heads if we want to
+ if head_mask is not None:
+ attention_probs = attention_probs * head_mask
+
+ 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,)
+ )
+
+ if self.is_decoder:
+ outputs = outputs + (past_key_value,)
+ return outputs
+
+
+class JinaBertSelfOutput(nn.Module):
+ 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 JinaBertAttention(nn.Module):
+ def __init__(self, config, position_embedding_type=None):
+ super().__init__()
+ self.self = JinaBertSelfAttention(
+ config, position_embedding_type=position_embedding_type
+ )
+ self.output = JinaBertSelfOutput(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: 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_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+ output_attentions: Optional[bool] = False,
+ bias: Optional[torch.FloatTensor] = None,
+ ) -> Tuple[torch.Tensor]:
+ self_outputs = self.self(
+ hidden_states,
+ attention_mask,
+ head_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ past_key_value,
+ output_attentions,
+ bias,
+ )
+ attention_output = self.output(self_outputs[0], hidden_states)
+ outputs = (attention_output,) + self_outputs[
+ 1:
+ ] # add attentions if we output them
+ return outputs
+
+
+class JinaBertIntermediate(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+ if isinstance(config.hidden_act, str):
+ self.intermediate_act_fn = ACT2FN[config.hidden_act]
+ else:
+ self.intermediate_act_fn = config.hidden_act
+
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+ hidden_states = self.dense(hidden_states)
+ hidden_states = self.intermediate_act_fn(hidden_states)
+ return hidden_states
+
+
+class JinaBertOutput(nn.Module):
+ def __init__(self, config: JinaBertConfig):
+ super().__init__()
+ self.dense = nn.Linear(config.intermediate_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 JinaBertGLUMLP(nn.Module):
+ def __init__(self, config: JinaBertConfig):
+ super().__init__()
+ self.config = config
+ self.gated_layers = nn.Linear(
+ config.hidden_size, config.intermediate_size * 2, bias=False
+ )
+ if config.feed_forward_type == 'reglu':
+ self.act = nn.ReLU()
+ elif config.feed_forward_type == 'geglu':
+ self.act = nn.GELU()
+ else:
+ raise ValueError(
+ f"feed_forward_type {config.feed_forward_type} not supported"
+ )
+ 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:
+ 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 JinaBertLayer(nn.Module):
+ def __init__(self, config: JinaBertConfig):
+ super().__init__()
+ self.chunk_size_feed_forward = config.chunk_size_feed_forward
+ self.seq_len_dim = 1
+ self.attention = JinaBertAttention(config)
+ self.is_decoder = config.is_decoder
+ self.add_cross_attention = config.add_cross_attention
+ self.feed_forward_type = config.feed_forward_type
+ if self.add_cross_attention:
+ if not self.is_decoder:
+ raise ValueError(
+ f"{self} should be used as a decoder model if cross attention is added"
+ )
+ self.crossattention = JinaBertAttention(
+ config, position_embedding_type="absolute"
+ )
+ if self.feed_forward_type.endswith('glu'):
+ self.mlp = JinaBertGLUMLP(config)
+ else:
+ self.intermediate = JinaBertIntermediate(config)
+ self.output = JinaBertOutput(config)
+
+ 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,
+ bias: Optional[torch.FloatTensor] = None,
+ past_key_value: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+ output_attentions: Optional[bool] = False,
+ ) -> Tuple[torch.Tensor]:
+ # 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,
+ bias=bias,
+ )
+ 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:
+ if not hasattr(self, "crossattention"):
+ raise ValueError(
+ 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
+
+ if self.feed_forward_type.endswith('glu'):
+ layer_output = self.mlp(attention_output)
+ else:
+ 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
+
+
+class JinaBertEncoder(nn.Module):
+ def __init__(self, config: JinaBertConfig):
+ super().__init__()
+ self.config = config
+ self.layer = nn.ModuleList(
+ [JinaBertLayer(config) for _ in range(config.num_hidden_layers)]
+ )
+ self.gradient_checkpointing = False
+ self.num_attention_heads = config.num_attention_heads
+ self.register_buffer(
+ "alibi",
+ self.rebuild_alibi_tensor(size=config.max_position_embeddings),
+ persistent=False,
+ )
+
+ 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):
+ start = 2 ** (-(2 ** -(math.log2(n) - 3)))
+ ratio = start
+ return [start * ratio**i for i in range(n)]
+
+ if math.log2(n_heads).is_integer():
+ return get_slopes_power_of_2(
+ n_heads
+ ) # In the paper, we only train models that have 2^a heads for some a. This function has
+ else: # some good properties that only occur when the input is a power of 2. To maintain that even
+ closest_power_of_2 = 2 ** math.floor(
+ math.log2(n_heads)
+ ) # when the number of heads is not a power of 2, we use this workaround.
+ return (
+ get_slopes_power_of_2(closest_power_of_2)
+ + _get_alibi_head_slopes(2 * closest_power_of_2)[0::2][
+ : n_heads - closest_power_of_2
+ ]
+ )
+
+ 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) * -1
+ 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
+ return 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,
+ ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
+ 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
+ )
+
+ # Add alibi matrix to extended_attention_mask
+ _, seqlen, _ = hidden_states.size()
+ 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.register_buffer(
+ "alibi",
+ self.rebuild_alibi_tensor(size=seqlen, device=hidden_states.device).to(
+ hidden_states.dtype
+ ),
+ persistent=False,
+ )
+ elif self.alibi.device != hidden_states.device:
+ # Device catch-up
+ self.alibi = self.alibi.to(hidden_states.device)
+
+ alibi_bias = self.alibi[:, :, :seqlen, :seqlen]
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ next_decoder_cache = () if use_cache 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 self.gradient_checkpointing and self.training:
+
+ 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,
+ alibi_bias,
+ )
+ else:
+ layer_outputs = layer_module(
+ hidden_states,
+ attention_mask,
+ layer_head_mask,
+ encoder_hidden_states,
+ encoder_attention_mask,
+ alibi_bias,
+ past_key_value,
+ output_attentions,
+ )
+
+ 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 JinaBertPooler(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) -> torch.Tensor:
+ # We "pool" the model by simply taking the hidden state corresponding
+ # to the first token.
+ first_token_tensor = hidden_states[:, 0]
+ pooled_output = self.dense(first_token_tensor)
+ pooled_output = self.activation(pooled_output)
+ return pooled_output
+
+
+class JinaBertPredictionHeadTransform(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 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+ 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 JinaBertLMPredictionHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.transform = JinaBertPredictionHeadTransform(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(config.hidden_size, config.vocab_size, bias=False)
+
+ self.bias = nn.Parameter(torch.zeros(config.vocab_size))
+
+ # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
+ self.decoder.bias = self.bias
+
+ def forward(self, hidden_states):
+ hidden_states = self.transform(hidden_states)
+ hidden_states = self.decoder(hidden_states)
+ return hidden_states
+
+
+class JinaBertOnlyMLMHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.predictions = JinaBertLMPredictionHead(config)
+
+ def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
+ prediction_scores = self.predictions(sequence_output)
+ return prediction_scores
+
+
+class JinaBertOnlyNSPHead(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+ def forward(self, pooled_output):
+ seq_relationship_score = self.seq_relationship(pooled_output)
+ return seq_relationship_score
+
+
+class JinaBertPreTrainingHeads(nn.Module):
+ def __init__(self, config):
+ super().__init__()
+ self.predictions = JinaBertLMPredictionHead(config)
+ self.seq_relationship = nn.Linear(config.hidden_size, 2)
+
+ def forward(self, sequence_output, pooled_output):
+ prediction_scores = self.predictions(sequence_output)
+ seq_relationship_score = self.seq_relationship(pooled_output)
+ return prediction_scores, seq_relationship_score
+
+
+class JinaBertPreTrainedModel(PreTrainedModel):
+ """
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+ models.
+ """
+
+ config_class = JinaBertConfig
+ load_tf_weights = load_tf_weights_in_bert
+ base_model_prefix = "bert"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["JinaBertLayer"]
+
+ 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, nn.LayerNorm):
+ module.bias.data.zero_()
+ module.weight.data.fill_(1.0)
+
+ def _set_gradient_checkpointing(self, module, value=False):
+ if isinstance(module, JinaBertEncoder):
+ module.gradient_checkpointing = value
+
+
+@dataclass
+class JinaBertForPreTrainingOutput(ModelOutput):
+ """
+ Output type of [`BertForPreTraining`].
+
+ Args:
+ loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
+ Total loss as the sum of the masked language modeling loss and the next sequence prediction
+ (classification) loss.
+ prediction_logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+ Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+ seq_relationship_logits (`torch.FloatTensor` of shape `(batch_size, 2)`):
+ Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
+ before SoftMax).
+ hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+ Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
+ shape `(batch_size, sequence_length, hidden_size)`.
+
+ Hidden-states of the model at the output of each layer plus the initial embedding outputs.
+ attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+ Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
+ sequence_length)`.
+
+ Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
+ heads.
+ """
+
+ loss: Optional[torch.FloatTensor] = None
+ prediction_logits: torch.FloatTensor = None
+ seq_relationship_logits: torch.FloatTensor = None
+ hidden_states: Optional[Tuple[torch.FloatTensor]] = None
+ attentions: Optional[Tuple[torch.FloatTensor]] = None
+
+
+BERT_START_DOCSTRING = r"""
+
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`BertConfig`]): Model configuration class with all the parameters of the model.
+ Initializing with a config file does not load the weights associated with the model, only the
+ configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+BERT_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `({0})`):
+ Indices of input sequence tokens in the vocabulary.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+ token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+ Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
+ 1]`:
+
+ - 0 corresponds to a *sentence A* token,
+ - 1 corresponds to a *sentence B* token.
+
+ [What are token type IDs?](../glossary#token-type-ids)
+ position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.max_position_embeddings - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+ Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+
+ inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare Bert Model transformer outputting raw hidden-states without any specific head on top.",
+ BERT_START_DOCSTRING,
+)
+class JinaBertModel(JinaBertPreTrainedModel):
+ """
+
+ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
+ cross-attention is added between the self-attention layers, following the architecture described in [Attention is
+ all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
+ Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
+
+ To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
+ to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
+ `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
+ """
+
+ def __init__(self, config: JinaBertConfig, add_pooling_layer=True):
+ super().__init__(config)
+ self.config = config
+
+ self.emb_pooler = config.emb_pooler
+ self._name_or_path = config._name_or_path
+ if self.emb_pooler:
+ from transformers import AutoTokenizer
+
+ self.tokenizer = AutoTokenizer.from_pretrained(config._name_or_path)
+
+ self.embeddings = JinaBertEmbeddings(config)
+ self.encoder = JinaBertEncoder(config)
+
+ self.pooler = JinaBertPooler(config) if add_pooling_layer else None
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @torch.inference_mode()
+ def encode(
+ self: 'JinaBertModel',
+ sentences: Union[str, List[str]],
+ batch_size: int = 32,
+ show_progress_bar: Optional[bool] = None,
+ output_value: str = 'sentence_embedding',
+ convert_to_numpy: bool = True,
+ convert_to_tensor: bool = False,
+ device: Optional[torch.device] = None,
+ normalize_embeddings: bool = False,
+ **tokenizer_kwargs,
+ ) -> Union[List[torch.Tensor], np.ndarray, torch.Tensor]:
+ """
+ Computes sentence embeddings
+
+ Args:
+ sentences(`str` or `List[str]`):
+ Sentence or sentences to be encoded
+ batch_size(`int`, *optional*, defaults to 32):
+ Batch size for the computation
+ show_progress_bar(`bool`, *optional*, defaults to None):
+ Show a progress bar when encoding sentences.
+ If set to None, progress bar is only shown when `logger.level == logging.INFO` or `logger.level == logging.DEBUG`.
+ output_value(`str`, *optional*, defaults to 'sentence_embedding'):
+ Default sentence_embedding, to get sentence embeddings.
+ Can be set to token_embeddings to get wordpiece token embeddings.
+ Set to None, to get all output values
+ convert_to_numpy(`bool`, *optional*, defaults to True):
+ If true, the output is a list of numpy vectors.
+ Else, it is a list of pytorch tensors.
+ convert_to_tensor(`bool`, *optional*, defaults to False):
+ If true, you get one large tensor as return.
+ Overwrites any setting from convert_to_numpy
+ device(`torch.device`, *optional*, defaults to None):
+ Which torch.device to use for the computation
+ normalize_embeddings(`bool`, *optional*, defaults to False):
+ If set to true, returned vectors will have length 1. In that case, the faster dot-product (util.dot_score) instead of cosine similarity can be used.
+ tokenizer_kwargs(`Dict[str, Any]`, *optional*, defaults to {}):
+ Keyword arguments for the tokenizer
+
+ Returns:
+ By default, a list of tensors is returned.
+ If convert_to_tensor, a stacked tensor is returned.
+ If convert_to_numpy, a numpy matrix is returned.
+ """
+ if not self.emb_pooler:
+ warnings.warn("No emb_pooler specified, defaulting to mean pooling.")
+ self.emb_pooler = 'mean'
+ from transformers import AutoTokenizer
+
+ self.tokenizer = AutoTokenizer.from_pretrained(self._name_or_path)
+ is_training = self.training
+ self.eval()
+
+ if show_progress_bar is None:
+ show_progress_bar = (
+ logger.getEffectiveLevel() == logging.INFO
+ or logger.getEffectiveLevel() == logging.DEBUG
+ )
+
+ if convert_to_tensor:
+ convert_to_numpy = False
+
+ if output_value != 'sentence_embedding':
+ convert_to_tensor = False
+ convert_to_numpy = False
+
+ input_was_string = False
+ if isinstance(sentences, str) or not hasattr(sentences, '__len__'):
+ sentences = [sentences]
+ input_was_string = True
+
+ if device is not None:
+ self.to(device)
+
+ # TODO: Maybe use better length heuristic?
+ permutation = np.argsort([-len(i) for i in sentences])
+ inverse_permutation = np.argsort(permutation)
+ sentences = [sentences[idx] for idx in permutation]
+
+ tokenizer_kwargs['padding'] = tokenizer_kwargs.get('padding', True)
+ tokenizer_kwargs['max_length'] = tokenizer_kwargs.get('max_length', 8192)
+ tokenizer_kwargs['truncation'] = tokenizer_kwargs.get('truncation', True)
+
+ all_embeddings = []
+
+ if has_tqdm:
+ range_iter = trange(
+ 0,
+ len(sentences),
+ batch_size,
+ desc="Encoding",
+ disable=not show_progress_bar,
+ )
+ else:
+ range_iter = range(0, len(sentences), batch_size)
+
+ for i in range_iter:
+ encoded_input = self.tokenizer(
+ sentences[i : i + batch_size],
+ return_tensors='pt',
+ **tokenizer_kwargs,
+ ).to(self.device)
+ token_embs = self.forward(**encoded_input)[0]
+
+ # Accumulate in fp32 to avoid overflow
+ token_embs = token_embs.float()
+
+ if output_value == 'token_embeddings':
+ raise NotImplementedError
+ elif output_value is None:
+ raise NotImplementedError
+ else:
+ embeddings = self.mean_pooling(
+ token_embs, encoded_input['attention_mask']
+ )
+
+ if normalize_embeddings:
+ embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
+
+ if convert_to_numpy:
+ embeddings = embeddings.cpu()
+ all_embeddings.extend(embeddings)
+
+ all_embeddings = [all_embeddings[idx] for idx in inverse_permutation]
+
+ if convert_to_tensor:
+ all_embeddings = torch.stack(all_embeddings)
+ elif convert_to_numpy:
+ all_embeddings = np.asarray([emb.numpy() for emb in all_embeddings])
+
+ if input_was_string:
+ all_embeddings = all_embeddings[0]
+
+ self.train(is_training)
+ return all_embeddings
+
+ def mean_pooling(
+ self, token_embeddings: torch.Tensor, attention_mask: torch.Tensor
+ ):
+ input_mask_expanded = (
+ attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+ )
+ return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(
+ input_mask_expanded.sum(1), min=1e-9
+ )
+
+ 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)
+
+ @add_start_docstrings_to_model_forward(
+ BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+ )
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=BaseModelOutputWithPoolingAndCrossAttentions,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ 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,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
+ r"""
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+ the model is configured as a decoder.
+ encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+ the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+ Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ """
+ output_attentions = (
+ 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 self.config.is_decoder:
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+ else:
+ use_cache = False
+
+ 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:
+ # self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
+ 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")
+
+ batch_size, seq_length = input_shape
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+ # past_key_values_length
+ past_key_values_length = (
+ past_key_values[0][0].shape[2] if past_key_values is not None else 0
+ )
+
+ if attention_mask is None:
+ attention_mask = torch.ones(
+ ((batch_size, seq_length + past_key_values_length)), device=device
+ )
+
+ if token_type_ids is None:
+ if hasattr(self.embeddings, "token_type_ids"):
+ buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
+ buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
+ batch_size, seq_length
+ )
+ token_type_ids = buffered_token_type_ids_expanded
+ else:
+ token_type_ids = torch.zeros(
+ input_shape, dtype=torch.long, device=device
+ )
+
+ # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+ # ourselves in which case we just need to make it broadcastable to all heads.
+ extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
+ attention_mask, input_shape
+ )
+
+ # If a 2D or 3D attention mask is provided for the cross-attention
+ # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+ if self.config.is_decoder and encoder_hidden_states is not None:
+ (
+ encoder_batch_size,
+ encoder_sequence_length,
+ _,
+ ) = encoder_hidden_states.size()
+ encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+ if encoder_attention_mask is None:
+ encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+ encoder_extended_attention_mask = self.invert_attention_mask(
+ encoder_attention_mask
+ )
+ else:
+ encoder_extended_attention_mask = None
+
+ # Prepare head mask if needed
+ # 1.0 in head_mask indicate we keep the head
+ # attention_probs has shape bsz x n_heads x N x N
+ # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+ # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+ embedding_output = self.embeddings(
+ input_ids=input_ids,
+ position_ids=position_ids,
+ token_type_ids=token_type_ids,
+ inputs_embeds=inputs_embeds,
+ past_key_values_length=past_key_values_length,
+ )
+ encoder_outputs = self.encoder(
+ embedding_output,
+ attention_mask=extended_attention_mask,
+ head_mask=head_mask,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_extended_attention_mask,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ 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 self.pooler is not None else None
+ )
+
+ if not return_dict:
+ return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+ 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,
+ )
+
+
+@add_start_docstrings(
+ """
+ Bert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
+ sentence prediction (classification)` head.
+ """,
+ BERT_START_DOCSTRING,
+)
+class JinaBertForPreTraining(JinaBertPreTrainedModel):
+ _tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.bert = JinaBertModel(config)
+ self.cls = JinaBertPreTrainingHeads(config)
+
+ # 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
+
+ @add_start_docstrings_to_model_forward(
+ BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+ )
+ @replace_return_docstrings(
+ output_type=JinaBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC
+ )
+ 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,
+ next_sentence_label: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple[torch.Tensor], JinaBertForPreTrainingOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels 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]`
+ next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the next sequence prediction (classification) loss. Input should be a sequence
+ pair (see `input_ids` docstring) Indices should be in `[0, 1]`:
+
+ - 0 indicates sequence B is a continuation of sequence A,
+ - 1 indicates sequence B is a random sequence.
+ kwargs (`Dict[str, any]`, optional, defaults to *{}*):
+ Used to hide legacy arguments that have been deprecated.
+
+ Returns:
+ """
+ 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,
+ )
+
+ sequence_output, pooled_output = outputs[:2]
+ prediction_scores, seq_relationship_score = self.cls(
+ sequence_output, pooled_output
+ )
+
+ total_loss = None
+ if labels is not None and next_sentence_label is not None:
+ loss_fct = CrossEntropyLoss()
+ masked_lm_loss = loss_fct(
+ prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
+ )
+ next_sentence_loss = loss_fct(
+ seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)
+ )
+ total_loss = masked_lm_loss + next_sentence_loss
+
+ if not return_dict:
+ output = (prediction_scores, seq_relationship_score) + outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return JinaBertForPreTrainingOutput(
+ loss=total_loss,
+ prediction_logits=prediction_scores,
+ seq_relationship_logits=seq_relationship_score,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@add_start_docstrings(
+ """JinaBert Model with a `language modeling` head on top for CLM fine-tuning.""",
+ BERT_START_DOCSTRING,
+)
+class JinaBertLMHeadModel(JinaBertPreTrainedModel):
+ _tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ if not config.is_decoder:
+ logger.warning(
+ "If you want to use `JinaBertLMHeadModel` as a standalone, add `is_decoder=True.`"
+ )
+
+ self.bert = JinaBertModel(config, add_pooling_layer=False)
+ self.cls = JinaBertOnlyMLMHead(config)
+
+ # 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
+
+ @add_start_docstrings_to_model_forward(
+ BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+ )
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=CausalLMOutputWithCrossAttentions,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ 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,
+ past_key_values: Optional[List[torch.Tensor]] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
+ r"""
+ encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+ the model is configured as a decoder.
+ encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+ the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the left-to-right language modeling loss (next word prediction). 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 n `[0, ..., config.vocab_size]`
+ past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+ Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ """
+ return_dict = (
+ return_dict if return_dict is not None else self.config.use_return_dict
+ )
+ if labels is not None:
+ use_cache = False
+
+ 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,
+ past_key_values=past_key_values,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ sequence_output = outputs[0]
+ prediction_scores = self.cls(sequence_output)
+
+ lm_loss = None
+ if labels is not None:
+ # we are doing next-token prediction; shift prediction scores and input ids by one
+ shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
+ labels = labels[:, 1:].contiguous()
+ loss_fct = CrossEntropyLoss()
+ lm_loss = loss_fct(
+ shifted_prediction_scores.view(-1, self.config.vocab_size),
+ labels.view(-1),
+ )
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return ((lm_loss,) + output) if lm_loss is not None else output
+
+ return CausalLMOutputWithCrossAttentions(
+ loss=lm_loss,
+ logits=prediction_scores,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ cross_attentions=outputs.cross_attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ attention_mask=None,
+ use_cache=True,
+ **model_kwargs,
+ ):
+ input_shape = input_ids.shape
+ # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+ if attention_mask is None:
+ attention_mask = input_ids.new_ones(input_shape)
+
+ # cut decoder_input_ids if past_key_values is used
+ if past_key_values is not None:
+ input_ids = input_ids[:, -1:]
+
+ return {
+ "input_ids": input_ids,
+ "attention_mask": attention_mask,
+ "past_key_values": past_key_values,
+ "use_cache": use_cache,
+ }
+
+ def _reorder_cache(self, past_key_values, beam_idx):
+ reordered_past = ()
+ for layer_past in past_key_values:
+ reordered_past += (
+ tuple(
+ past_state.index_select(0, beam_idx) for past_state in layer_past
+ ),
+ )
+ return reordered_past
+
+
+@add_start_docstrings(
+ """JinaBert Model with a `language modeling` head on top.""", BERT_START_DOCSTRING
+)
+class JinaBertForMaskedLM(JinaBertPreTrainedModel):
+ _tied_weights_keys = ["predictions.decoder.bias", "cls.predictions.decoder.weight"]
+
+ def __init__(self, config):
+ super().__init__(config)
+
+ if config.is_decoder:
+ logger.warning(
+ "If you want to use `JinaBertForMaskedLM` make sure `config.is_decoder=False` for "
+ "bi-directional self-attention."
+ )
+
+ self.bert = JinaBertModel(config, add_pooling_layer=False)
+ self.cls = JinaBertOnlyMLMHead(config)
+
+ # 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
+
+ @add_start_docstrings_to_model_forward(
+ BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+ )
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=MaskedLMOutput,
+ config_class=_CONFIG_FOR_DOC,
+ expected_output="'paris'",
+ expected_loss=0.88,
+ )
+ 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]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels 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]`
+ """
+
+ 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)
+
+ masked_lm_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss() # -100 index = padding token
+ masked_lm_loss = loss_fct(
+ prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)
+ )
+
+ if not return_dict:
+ output = (prediction_scores,) + outputs[2:]
+ return (
+ ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
+ )
+
+ return MaskedLMOutput(
+ loss=masked_lm_loss,
+ logits=prediction_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+ def prepare_inputs_for_generation(
+ self, input_ids, attention_mask=None, **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}
+
+
+@add_start_docstrings(
+ """JinaBert Model with a `next sentence prediction (classification)` head on top.""",
+ BERT_START_DOCSTRING,
+)
+class JinaBertForNextSentencePrediction(JinaBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.bert = JinaBertModel(config)
+ self.cls = JinaBertOnlyNSPHead(config)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @add_start_docstrings_to_model_forward(
+ BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+ )
+ @replace_return_docstrings(
+ output_type=NextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC
+ )
+ 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,
+ **kwargs,
+ ) -> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
+ (see `input_ids` docstring). Indices should be in `[0, 1]`:
+
+ - 0 indicates sequence B is a continuation of sequence A,
+ - 1 indicates sequence B is a random sequence.
+
+ Returns:
+ """
+
+ if "next_sentence_label" in kwargs:
+ warnings.warn(
+ "The `next_sentence_label` argument is deprecated and will be removed in a future version, use"
+ " `labels` instead.",
+ FutureWarning,
+ )
+ labels = kwargs.pop("next_sentence_label")
+
+ 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]
+
+ seq_relationship_scores = self.cls(pooled_output)
+
+ next_sentence_loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ next_sentence_loss = loss_fct(
+ seq_relationship_scores.view(-1, 2), labels.view(-1)
+ )
+
+ if not return_dict:
+ output = (seq_relationship_scores,) + outputs[2:]
+ return (
+ ((next_sentence_loss,) + output)
+ if next_sentence_loss is not None
+ else output
+ )
+
+ return NextSentencePredictorOutput(
+ loss=next_sentence_loss,
+ logits=seq_relationship_scores,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@add_start_docstrings(
+ """
+ JinaBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled
+ output) e.g. for GLUE tasks.
+ """,
+ BERT_START_DOCSTRING,
+)
+class JinaBertForSequenceClassification(JinaBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.config = config
+
+ self.bert = JinaBertModel(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()
+
+ @add_start_docstrings_to_model_forward(
+ BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+ )
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_SEQUENCE_CLASSIFICATION,
+ output_type=SequenceClassifierOutput,
+ config_class=_CONFIG_FOR_DOC,
+ expected_output=_SEQ_CLASS_EXPECTED_OUTPUT,
+ expected_loss=_SEQ_CLASS_EXPECTED_LOSS,
+ )
+ 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]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = (
+ return_dict if return_dict is not None else self.config.use_return_dict
+ )
+
+ outputs = self.bert(
+ input_ids,
+ 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:
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (
+ labels.dtype == torch.long or labels.dtype == torch.int
+ ):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(logits, labels)
+ if not return_dict:
+ output = (logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutput(
+ loss=loss,
+ logits=logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@add_start_docstrings(
+ """
+ JinaBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
+ softmax) e.g. for RocStories/SWAG tasks.
+ """,
+ BERT_START_DOCSTRING,
+)
+class JinaBertForMultipleChoice(JinaBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+
+ self.bert = JinaBertModel(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, 1)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @add_start_docstrings_to_model_forward(
+ BERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
+ )
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_DOC,
+ output_type=MultipleChoiceModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ )
+ 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], MultipleChoiceModelOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
+ num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
+ `input_ids` above)
+ """
+ return_dict = (
+ return_dict if return_dict is not None else self.config.use_return_dict
+ )
+ num_choices = (
+ input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
+ )
+
+ input_ids = (
+ input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
+ )
+ attention_mask = (
+ attention_mask.view(-1, attention_mask.size(-1))
+ if attention_mask is not None
+ else None
+ )
+ token_type_ids = (
+ token_type_ids.view(-1, token_type_ids.size(-1))
+ if token_type_ids is not None
+ else None
+ )
+ position_ids = (
+ position_ids.view(-1, position_ids.size(-1))
+ if position_ids is not None
+ else None
+ )
+ inputs_embeds = (
+ inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
+ if inputs_embeds is not None
+ else None
+ )
+
+ 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)
+ reshaped_logits = logits.view(-1, num_choices)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(reshaped_logits, labels)
+
+ if not return_dict:
+ output = (reshaped_logits,) + outputs[2:]
+ return ((loss,) + output) if loss is not None else output
+
+ return MultipleChoiceModelOutput(
+ loss=loss,
+ logits=reshaped_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+
+
+@add_start_docstrings(
+ """
+ JinaBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
+ Named-Entity-Recognition (NER) tasks.
+ """,
+ BERT_START_DOCSTRING,
+)
+class JinaBertForTokenClassification(JinaBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.bert = JinaBertModel(config, add_pooling_layer=False)
+ 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()
+
+ @add_start_docstrings_to_model_forward(
+ BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+ )
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_TOKEN_CLASSIFICATION,
+ output_type=TokenClassifierOutput,
+ config_class=_CONFIG_FOR_DOC,
+ expected_output=_TOKEN_CLASS_EXPECTED_OUTPUT,
+ expected_loss=_TOKEN_CLASS_EXPECTED_LOSS,
+ )
+ 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], TokenClassifierOutput]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
+ """
+ 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,
+ )
+
+ sequence_output = outputs[0]
+
+ sequence_output = self.dropout(sequence_output)
+ logits = self.classifier(sequence_output)
+
+ loss = None
+ if labels is not None:
+ loss_fct = CrossEntropyLoss()
+ 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,
+ )
+
+
+@add_start_docstrings(
+ """
+ JinaBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
+ layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
+ """,
+ BERT_START_DOCSTRING,
+)
+class JinaBertForQuestionAnswering(JinaBertPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+
+ self.bert = JinaBertModel(config, add_pooling_layer=False)
+ self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ @add_start_docstrings_to_model_forward(
+ BERT_INPUTS_DOCSTRING.format("batch_size, sequence_length")
+ )
+ @add_code_sample_docstrings(
+ checkpoint=_CHECKPOINT_FOR_QA,
+ output_type=QuestionAnsweringModelOutput,
+ config_class=_CONFIG_FOR_DOC,
+ qa_target_start_index=_QA_TARGET_START_INDEX,
+ qa_target_end_index=_QA_TARGET_END_INDEX,
+ expected_output=_QA_EXPECTED_OUTPUT,
+ expected_loss=_QA_EXPECTED_LOSS,
+ )
+ 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,
+ start_positions: Optional[torch.Tensor] = None,
+ end_positions: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
+ r"""
+ start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for position (index) of the start of the labelled span for computing the token classification loss.
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+ are not taken into account for computing the loss.
+ end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for position (index) of the end of the labelled span for computing the token classification loss.
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+ are not taken into account for computing the loss.
+ """
+ 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,
+ )
+
+ sequence_output = outputs[0]
+
+ logits = self.qa_outputs(sequence_output)
+ start_logits, end_logits = logits.split(1, dim=-1)
+ start_logits = start_logits.squeeze(-1).contiguous()
+ end_logits = end_logits.squeeze(-1).contiguous()
+
+ total_loss = None
+ if start_positions is not None and end_positions is not None:
+ # If we are on multi-GPU, split add a dimension
+ if len(start_positions.size()) > 1:
+ start_positions = start_positions.squeeze(-1)
+ if len(end_positions.size()) > 1:
+ end_positions = end_positions.squeeze(-1)
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
+ ignored_index = start_logits.size(1)
+ start_positions = start_positions.clamp(0, ignored_index)
+ end_positions = end_positions.clamp(0, ignored_index)
+
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+ start_loss = loss_fct(start_logits, start_positions)
+ end_loss = loss_fct(end_logits, end_positions)
+ total_loss = (start_loss + end_loss) / 2
+
+ if not return_dict:
+ output = (start_logits, end_logits) + outputs[2:]
+ return ((total_loss,) + output) if total_loss is not None else output
+
+ return QuestionAnsweringModelOutput(
+ loss=total_loss,
+ start_logits=start_logits,
+ end_logits=end_logits,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ )
+