jina-embeddings-v2-base-en / modeling_jbert.py
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# Copyright 2022 MosaicML Examples authors
# SPDX-License-Identifier: Apache-2.0
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018-2021, NVIDIA CORPORATION. All rights reserved.
# Copyright (c) 2022, Tri Dao.
import copy
import logging
import math
import warnings
from typing import List, Optional, Tuple, Union
import torch
import torch.nn as nn
from einops import rearrange
from transformers.activations import ACT2FN
from transformers.modeling_outputs import (
MaskedLMOutput,
SequenceClassifierOutput,
BaseModelOutputWithPastAndCrossAttentions,
BaseModelOutputWithPoolingAndCrossAttentions,
)
from transformers.models.bert.modeling_bert import BertPreTrainedModel
from .bert_padding import (index_first_axis, index_put_first_axis, pad_input,
unpad_input, unpad_input_only)
from .configuration_jbert import JBertConfig
logger = logging.getLogger(__name__)
class JBertEmbeddings(nn.Module):
"""Construct the embeddings for words, ignoring position.
There are no positional embeddings since we use ALiBi and token_type
embeddings.
This module is modeled after the Hugging Face BERT's
:class:`~transformers.model.bert.modeling_bert.BertEmbeddings`, but is
modified to implement ALiBi. The key change is
that position embeddings are removed. Position information instead comes
from attention biases that scale linearly with the position distance
between query and key tokens.
This module ignores the `position_ids` input to the `forward` method.
"""
def __init__(self, config):
super().__init__()
self.word_embeddings = nn.Embedding(
config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id
)
# ALiBi doesn't use position embeddings
self.token_type_embeddings = nn.Embedding(
config.type_vocab_size, config.hidden_size
)
# self.LayerNorm is not snake-cased to stick with TensorFlow model
# variable name and be able to load any TensorFlow checkpoint file
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.register_buffer(
"token_type_ids", torch.zeros((1, config.model_max_length), dtype=torch.long), persistent=False
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
past_key_values_length: int = 0,
) -> torch.Tensor:
if (input_ids is not None) == (inputs_embeds is not None):
raise ValueError('Must specify either input_ids or input_embeds!')
if input_ids is not None:
input_shape = input_ids.size()
else:
assert inputs_embeds is not None # just for type checking
input_shape = inputs_embeds.size()[:-1]
seq_length = input_shape[1]
if position_ids is not None:
warnings.warn('position_ids is not used in JBertEmbeddings as it does not have position embeddings.')
# Setting the token_type_ids to the registered buffer in constructor
# where it is all zeros, which usually occurs when it's auto-generated;
# registered buffer helps users when tracing the model without passing
# token_type_ids, solves issue #5664
if token_type_ids is None:
if hasattr(self, 'token_type_ids'):
buffered_token_type_ids = self.token_type_ids[:, :seq_length]
buffered_token_type_ids_expanded = buffered_token_type_ids.expand(
input_shape[0], seq_length
)
token_type_ids = buffered_token_type_ids_expanded # type: ignore
else:
token_type_ids = torch.zeros(
input_shape, # type: ignore
dtype=torch.long,
device=self.word_embeddings.device,
) # type: ignore # yapf: disable
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class BertUnpadSelfAttention(nn.Module):
"""Performs multi-headed self attention on a batch of unpadded sequences.
If Triton is installed, this module uses Flash Attention to greatly improve throughput.
The Flash Attention implementation used in Mosaic BERT supports arbitrary attention biases (which
we use to implement ALiBi), but does not support attention dropout. If either Triton is not installed
or `config.attention_probs_dropout_prob > 0`, the implementation will default to a
math-equivalent pytorch version, which is much slower.
See `forward` method for additional detail.
"""
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
config, 'embedding_size'
):
raise ValueError(
f'The hidden size ({config.hidden_size}) is not a multiple of the number of attention '
f'heads ({config.num_attention_heads})'
)
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
# TODO: self.all_head_size == config.hidden_size? Why not just use config.hidden_size?
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.Wqkv = nn.Linear(self.all_head_size, 3 * config.hidden_size)
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(
self,
hidden_states: torch.Tensor,
cu_seqlens: torch.Tensor,
max_seqlen_in_batch: int,
indices: torch.Tensor,
attn_mask: torch.Tensor,
bias: torch.Tensor,
) -> torch.Tensor:
"""Perform self-attention.
If dropout is zero, then we can use the Triton kernel, so we do that. However, if not, we send through a standard PyTorch
implementation of self-attention.
The arguments are unpadded, and our implementations of attention require padded arguments,
so we first call `pad_input`. Once we compute attention, we re-unpad our outputs for the other layers.
The pad/unpad operations add overhead, but not sending pad tokens through ffs saves compute.
It is possible to write an unpadded implementation of attention (in Triton and PyTorch), which we will eventually do.
Args:
hidden_states: (total_nnz, dim)
cu_seqlens: (batch + 1,)
max_seqlen_in_batch: int
indices: (total_nnz,)
attn_mask: (batch, max_seqlen_in_batch)
bias: (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
Returns:
attention: (total_nnz, dim)
"""
qkv = self.Wqkv(hidden_states)
qkv = pad_input(
qkv, indices, cu_seqlens.shape[0] - 1, max_seqlen_in_batch
) # batch, max_seqlen_in_batch, thd
qkv = rearrange(
qkv, 'b s (t h d) -> b s t h d', t=3, h=self.num_attention_heads
)
# if we have nonzero attention dropout (e.g. during fine-tuning) or no Triton, compute attention in PyTorch
q = qkv[:, :, 0, :, :].permute(0, 2, 1, 3) # b h s d
k = qkv[:, :, 1, :, :].permute(0, 2, 3, 1) # b h d s
v = qkv[:, :, 2, :, :].permute(0, 2, 1, 3) # b h s d
attention_scores = torch.matmul(q, k) / math.sqrt(self.attention_head_size)
attention_scores = attention_scores + bias
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
attention_probs = self.dropout(attention_probs)
attention_probs = attention_probs.to(dtype=v.dtype)
attention = torch.matmul(attention_probs, v).permute(0, 2, 1, 3) # b s h
# attn_mask is 1 for attend and 0 for don't
attention = unpad_input_only(attention, torch.squeeze(attn_mask) == 1)
return rearrange(attention, 'nnz h d -> nnz (h d)')
# Copy of transformer's library BertSelfOutput that will not be caught by surgery methods looking for HF BERT modules.
class BertSelfOutput(nn.Module):
"""Computes the output of the attention layer.
This module is modeled after the Hugging Face BERT's
:class:`~transformers.model.bert.modeling_bert.BertSelfOutput`.
The implementation is identical. Rather than use the original module
directly, we re-implement it here so that Mosaic BERT's modules will not
be affected by any Composer surgery algorithm that modifies Hugging Face
BERT modules.
"""
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(
self, hidden_states: torch.Tensor, input_tensor: torch.Tensor
) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class BertUnpadAttention(nn.Module):
"""Chains attention, Dropout, and LayerNorm for Mosaic BERT."""
def __init__(self, config):
super().__init__()
self.self = BertUnpadSelfAttention(config)
self.output = BertSelfOutput(config)
def forward(
self,
input_tensor: torch.Tensor,
cu_seqlens: torch.Tensor,
max_s: int,
subset_idx: Optional[torch.Tensor] = None,
indices: Optional[torch.Tensor] = None,
attn_mask: Optional[torch.Tensor] = None,
bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Forward pass for scaled self-attention without padding.
Arguments:
input_tensor: (total_nnz, dim)
cu_seqlens: (batch + 1,)
max_s: int
subset_idx: () set of indices whose values we care about at the end of the layer
(e.g., the masked tokens, if this is the final layer).
indices: None or (total_nnz,)
attn_mask: None or (batch, max_seqlen_in_batch)
bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
"""
self_output = self.self(
input_tensor, cu_seqlens, max_s, indices, attn_mask, bias
)
if subset_idx is not None:
return self.output(
index_first_axis(self_output, subset_idx),
index_first_axis(input_tensor, subset_idx),
)
else:
return self.output(self_output, input_tensor)
class BertGatedLinearUnitMLP(nn.Module):
"""Applies the FFN at the end of each Mosaic BERT layer.
Compared to the default BERT architecture, this block replaces :class:`~transformers.model.bert.modeling_bert.BertIntermediate`
and :class:`~transformers.model.bert.modeling_bert.SelfOutput` with a single module that has similar functionality, but
introduces Gated Linear Units.
Note: Mosaic BERT adds parameters in order to implement Gated Linear Units. To keep parameter count consistent with that of a
standard Hugging Face BERT, scale down `config.intermediate_size` by 2/3. For example, a Mosaic BERT constructed with
`config.intermediate_size=2048` will have the same parameter footprint as its Hugging Face BERT counterpart constructed
with the `config.intermediate_size=3072`.
However, in most cases it will not be necessary to adjust `config.intermediate_size` since, despite the increased
parameter size, Mosaic BERT typically offers a net higher throughput than a Hugging Face BERT built from the same `config`.
"""
def __init__(self, config):
super().__init__()
self.config = config
self.gated_layers = nn.Linear(
config.hidden_size, config.intermediate_size * 2, bias=False
)
self.act = nn.GELU(approximate='none')
self.wo = nn.Linear(config.intermediate_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
"""Compute new hidden states from current hidden states.
Args:
hidden_states (torch.Tensor): The (unpadded) hidden states from
the attention layer [nnz, dim].
"""
residual_connection = hidden_states
# compute the activation
hidden_states = self.gated_layers(hidden_states)
gated = hidden_states[:, : self.config.intermediate_size]
non_gated = hidden_states[:, self.config.intermediate_size :]
hidden_states = self.act(gated) * non_gated
hidden_states = self.dropout(hidden_states)
# multiply by the second matrix
hidden_states = self.wo(hidden_states)
# add the residual connection and post-LN
hidden_states = self.layernorm(hidden_states + residual_connection)
return hidden_states
class BertLayer(nn.Module):
"""Composes the Mosaic BERT attention and FFN blocks into a single layer."""
def __init__(self, config: JBertConfig):
super().__init__()
self.attention = BertUnpadAttention(config)
self.mlp = BertGatedLinearUnitMLP(config)
def forward(
self,
hidden_states: torch.Tensor,
cu_seqlens: torch.Tensor,
seqlen: int,
subset_idx: Optional[torch.Tensor] = None,
indices: Optional[torch.Tensor] = None,
attn_mask: Optional[torch.Tensor] = None,
bias: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Forward pass for a BERT layer, including both attention and MLP.
Args:
hidden_states: (total_nnz, dim)
cu_seqlens: (batch + 1,)
seqlen: int
subset_idx: () set of indices whose values we care about at the end of the layer
(e.g., the masked tokens, if this is the final layer).
indices: None or (total_nnz,)
attn_mask: None or (batch, max_seqlen_in_batch)
bias: None or (batch, heads, max_seqlen_in_batch, max_seqlen_in_batch)
"""
attention_output = self.attention(
hidden_states, cu_seqlens, seqlen, subset_idx, indices, attn_mask, bias
)
layer_output = self.mlp(attention_output)
return layer_output
class JBertEncoder(nn.Module):
"""A stack of BERT layers providing the backbone.
This module is modeled after the Hugging Face BERT's :class:`~transformers.model.bert.modeling_bert.BertEncoder`,
but with substantial modifications to implement unpadding and ALiBi.
Compared to the analogous Hugging Face BERT module, this module handles unpadding to reduce unnecessary computation
at padded tokens, and pre-computes attention biases to implement ALiBi.
"""
def __init__(self, config: JBertConfig):
super().__init__()
self.layer = nn.ModuleList(
[BertLayer(config) for _ in range(config.num_hidden_layers)]
)
self.num_attention_heads = config.num_attention_heads
# The alibi mask will be dynamically expanded if it is too small for
# the input the model receives. But it generally helps to initialize it
# to a reasonably large size to help pre-allocate CUDA memory.
# The default `model_max_length` is 8192.
self._current_alibi_size = int(config.model_max_length)
self.alibi = torch.zeros(
(
1,
self.num_attention_heads,
self._current_alibi_size,
self._current_alibi_size,
)
)
self.rebuild_alibi_tensor(size=config.model_max_length)
def rebuild_alibi_tensor(
self, size: int, device: Optional[Union[torch.device, str]] = None
):
# Alibi
# Following https://github.com/ofirpress/attention_with_linear_biases/issues/5 (Implementation 1)
# In the causal case, you can exploit the fact that softmax is invariant to a uniform translation
# of the logits, which makes the math work out *after* applying causal masking. If no causal masking
# will be applied, it is necessary to construct the diagonal mask.
n_heads = self.num_attention_heads
def _get_alibi_head_slopes(n_heads: int) -> List[float]:
def get_slopes_power_of_2(n_heads: int) -> List[float]:
start = 2 ** (-(2 ** -(math.log2(n_heads) - 3)))
ratio = start
return [start * ratio**i for i in range(n_heads)]
# In the paper, they only train models that have 2^a heads for some a. This function
# has some good properties that only occur when the input is a power of 2. To
# maintain that even when the number of heads is not a power of 2, we use a
# workaround.
if math.log2(n_heads).is_integer():
return get_slopes_power_of_2(n_heads)
closest_power_of_2 = 2 ** math.floor(math.log2(n_heads))
slopes_a = get_slopes_power_of_2(closest_power_of_2)
slopes_b = _get_alibi_head_slopes(2 * closest_power_of_2)
slopes_b = slopes_b[0::2][: n_heads - closest_power_of_2]
return slopes_a + slopes_b
context_position = torch.arange(size, device=device)[:, None]
memory_position = torch.arange(size, device=device)[None, :]
relative_position = torch.abs(memory_position - context_position)
# [n_heads, max_token_length, max_token_length]
relative_position = relative_position.unsqueeze(0).expand(n_heads, -1, -1)
slopes = torch.Tensor(_get_alibi_head_slopes(n_heads)).to(device)
alibi = slopes.unsqueeze(1).unsqueeze(1) * -relative_position
# [1, n_heads, max_token_length, max_token_length]
alibi = alibi.unsqueeze(0)
assert alibi.shape == torch.Size([1, n_heads, size, size])
self._current_alibi_size = size
self.alibi = alibi
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> List[torch.Tensor]:
all_hidden_states = [] if output_hidden_states else None
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
attention_mask_bool = attention_mask.bool()
batch, seqlen = hidden_states.shape[:2]
# Unpad inputs and mask. It will remove tokens that are padded.
# Assume ntokens is total number of tokens (padded and non-padded)
# and ntokens_unpad is total number of non-padded tokens.
# Then unpadding performs the following compression of the inputs:
# hidden_states[ntokens,hidden] -> hidden_states[ntokens_unpad,hidden]
hidden_states, indices, cu_seqlens, _ = unpad_input(
hidden_states, attention_mask_bool
)
# Add alibi matrix to extended_attention_mask
if self._current_alibi_size < seqlen:
# Rebuild the alibi tensor when needed
warnings.warn(
f'Increasing alibi size from {self._current_alibi_size} to {seqlen}'
)
self.rebuild_alibi_tensor(size=seqlen, device=hidden_states.device)
elif self.alibi.device != hidden_states.device:
# Device catch-up
self.alibi = self.alibi.to(hidden_states.device)
alibi_bias = self.alibi[:, :, :seqlen, :seqlen]
attn_bias = extended_attention_mask[:, :, :seqlen, :seqlen]
alibi_attn_mask = attn_bias + alibi_bias
for layer_module in self.layer:
if output_hidden_states:
all_hidden_states.append(rearrange(hidden_states, '(b n) d -> b n d', b=batch))
hidden_states = layer_module(
hidden_states,
cu_seqlens,
seqlen,
None,
indices,
attn_mask=attention_mask,
bias=alibi_attn_mask,
)
# Pad inputs and mask. It will insert back zero-padded tokens.
# Assume ntokens is total number of tokens (padded and non-padded)
# and ntokens_unpad is total number of non-padded tokens.
# Then padding performs the following de-compression:
# hidden_states[ntokens_unpad,hidden] -> hidden_states[ntokens,hidden]
hidden_states = pad_input(hidden_states, indices, batch, seqlen)
if output_hidden_states:
all_hidden_states.append(hidden_states)
if not return_dict:
return tuple(
v for v in [hidden_states, all_hidden_states] if v is not None
)
return BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=None,
hidden_states=all_hidden_states,
attentions=None,
cross_attentions=None,
)
class JBertPooler(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(
self, hidden_states: torch.Tensor, pool: Optional[bool] = True
) -> torch.Tensor:
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0] if pool else hidden_states
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class BertPredictionHeadTransform(nn.Module):
def __init__(self, config):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = torch.nn.LayerNorm(config.hidden_size, eps=1e-12)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class JBertModel(BertPreTrainedModel):
"""Overall BERT model.
Args:
config: a JBertConfig class instance with the configuration to build a new model
Inputs:
`input_ids`: a torch.LongTensor of shape [batch_size, sequence_length]
with the word token indices in the vocabulary(see the tokens preprocessing logic in the scripts
`extract_features.py`, `run_classifier.py` and `run_squad.py`)
`token_type_ids`: an optional torch.LongTensor of shape [batch_size, sequence_length] with the token
types indices selected in [0, 1]. Type 0 corresponds to a `sentence A` and type 1 corresponds to
a `sentence B` token (see BERT paper for more details).
`attention_mask`: an optional torch.LongTensor of shape [batch_size, sequence_length] with indices
selected in [0, 1]. It's a mask to be used if the input sequence length is smaller than the max
input sequence length in the current batch. It's the mask that we typically use for attention when
a batch has varying length sentences.
`output_all_encoded_layers`: boolean which controls the content of the `encoded_layers` output as described below. Default: `True`.
Outputs: Tuple of (encoded_layers, pooled_output)
`encoded_layers`: controlled by `output_all_encoded_layers` argument:
- `output_all_encoded_layers=True`: outputs a list of the full sequences of encoded-hidden-states at the end
of each attention block (i.e. 12 full sequences for BERT-base, 24 for BERT-large), each
encoded-hidden-state is a torch.FloatTensor of size [batch_size, sequence_length, hidden_size],
- `output_all_encoded_layers=False`: outputs only the full sequence of hidden-states corresponding
to the last attention block of shape [batch_size, sequence_length, hidden_size],
`pooled_output`: a torch.FloatTensor of size [batch_size, hidden_size] which is the output of a
classifier pretrained on top of the hidden state associated to the first character of the
input (`CLS`) to train on the Next-Sentence task (see BERT's paper).
Example usage:
```python
# Already been converted into WordPiece token ids
input_ids = torch.LongTensor([[31, 51, 99], [15, 5, 0]])
input_mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
token_type_ids = torch.LongTensor([[0, 0, 1], [0, 1, 0]])
config = modeling.JBertConfig(vocab_size_or_config_json_file=32000, hidden_size=768,
num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072)
model = JBertModel(config=config)
all_encoder_layers, pooled_output = model(input_ids, token_type_ids, input_mask)
```
"""
config_class = JBertConfig
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.embeddings = JBertEmbeddings(config)
self.encoder = JBertEncoder(config)
self.pooler = JBertPooler(config) if add_pooling_layer else None
self.post_init()
def get_input_embeddings(self):
return self.embeddings.word_embeddings
def set_input_embeddings(self, value):
self.embeddings.word_embeddings = value
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
if attention_mask is None:
attention_mask = torch.ones_like(input_ids)
if token_type_ids is None:
token_type_ids = torch.zeros_like(input_ids)
embedding_output = self.embeddings(input_ids, token_type_ids, position_ids)
encoder_outputs: BaseModelOutputWithPastAndCrossAttentions = self.encoder(
hidden_states=embedding_output,
attention_mask=attention_mask,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
pooled_output = (
self.pooler(sequence_output) if self.pooler is not None else None
)
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
#return encoder_outputs, None
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
cross_attentions=encoder_outputs.cross_attentions,
)
###################
# Bert Heads
###################
class BertLMPredictionHead(nn.Module):
def __init__(self, config, bert_model_embedding_weights):
super().__init__()
self.transform = BertPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(
bert_model_embedding_weights.size(1), bert_model_embedding_weights.size(0)
)
self.decoder.weight = bert_model_embedding_weights
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
class BertOnlyMLMHead(nn.Module):
def __init__(self, config, bert_model_embedding_weights):
super().__init__()
self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class BertOnlyNSPHead(nn.Module):
def __init__(self, config):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output: torch.Tensor) -> torch.Tensor:
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
#####################
# Various Bert models
#####################
class JBertForMaskedLM(BertPreTrainedModel):
config_class = JBertConfig
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
warnings.warn(
'If you want to use `JBertForMaskedLM` make sure `config.is_decoder=False` for '
'bi-directional self-attention.'
)
self.bert = JBertModel(config, add_pooling_layer=False)
self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight)
# Initialize weights and apply final processing
self.post_init()
def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
# labels should be a `torch.LongTensor` of shape
# `(batch_size, sequence_length)`. These are used for computing the
# masked language modeling loss.
#
# Indices should be in `[-100, 0, ..., config.vocab_size]` (see
# `input_ids` docstring) Tokens with indices set to `-100` are ignored
# (masked), the loss is only computed for the tokens with labels in `[0,
# ..., config.vocab_size]`
#
# Prediction scores are only computed for masked tokens and the (bs,
# seqlen) dimensions are flattened
if (input_ids is not None) == (inputs_embeds is not None):
raise ValueError('Must specify either input_ids or input_embeds!')
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.cls(sequence_output)
loss = None
if labels is not None:
# Compute loss
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return MaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def prepare_inputs_for_generation(
self, input_ids: torch.Tensor, attention_mask: torch.Tensor, **model_kwargs
):
input_shape = input_ids.shape
effective_batch_size = input_shape[0]
# add a dummy token
if self.config.pad_token_id is None:
raise ValueError('The PAD token should be defined for generation')
attention_mask = torch.cat(
[attention_mask, attention_mask.new_zeros((attention_mask.shape[0], 1))],
dim=-1,
)
dummy_token = torch.full(
(effective_batch_size, 1),
self.config.pad_token_id,
dtype=torch.long,
device=input_ids.device,
)
input_ids = torch.cat([input_ids, dummy_token], dim=1)
return {'input_ids': input_ids, 'attention_mask': attention_mask}
class JBertForSequenceClassification(BertPreTrainedModel):
"""Bert Model transformer with a sequence classification/regression head.
This head is just a linear layer on top of the pooled output. Used for,
e.g., GLUE tasks.
"""
config_class = JBertConfig
def __init__(self, config):
super().__init__(config)
self.num_labels = config.num_labels
self.config = config
self.bert = JBertModel(config)
classifier_dropout = (
config.classifier_dropout
if config.classifier_dropout is not None
else config.hidden_dropout_prob
)
self.dropout = nn.Dropout(classifier_dropout)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
# labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
# Labels for computing the sequence classification/regression loss.
# Indices should be in `[0, ..., config.num_labels - 1]`.
# If `config.num_labels == 1` a regression loss is computed
# (mean-square loss). If `config.num_labels > 1` a classification loss
# is computed (cross-entropy).
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
outputs = self.bert(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
# Compute loss
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = 'regression'
elif self.num_labels > 1 and (
labels.dtype == torch.long or labels.dtype == torch.int
):
self.config.problem_type = 'single_label_classification'
else:
self.config.problem_type = 'multi_label_classification'
if self.config.problem_type == 'regression':
loss_fct = nn.MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == 'single_label_classification':
loss_fct = nn.CrossEntropyLoss()
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == 'multi_label_classification':
loss_fct = nn.BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=None,
attentions=None,
)