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modeling_canine.py
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modeling_canine.py
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# coding=utf-8
# Copyright 2021 Google AI The HuggingFace Inc. team. 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 CANINE model."""
import copy
import math
import os
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (
BaseModelOutput,
ModelOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_canine import CanineConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "google/canine-s"
_CONFIG_FOR_DOC = "CanineConfig"
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/canine-s",
"google/canine-r"
# See all CANINE models at https://huggingface.co/models?filter=canine
]
# Support up to 16 hash functions.
_PRIMES = [31, 43, 59, 61, 73, 97, 103, 113, 137, 149, 157, 173, 181, 193, 211, 223]
@dataclass
class CanineModelOutputWithPooling(ModelOutput):
"""
Output type of [`CanineModel`]. Based on [`~modeling_outputs.BaseModelOutputWithPooling`], but with slightly
different `hidden_states` and `attentions`, as these also include the hidden states and attentions of the shallow
Transformer encoders.
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model (i.e. the output of the final
shallow Transformer encoder).
pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
Hidden-state of the first token of the sequence (classification token) at the last layer of the deep
Transformer encoder, further processed by a Linear layer and a Tanh activation function. The Linear layer
weights are trained from the next sentence prediction (classification) objective during pretraining.
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 input to each encoder + one for the output of each layer of each
encoder) of shape `(batch_size, sequence_length, hidden_size)` and `(batch_size, sequence_length //
config.downsampling_rate, hidden_size)`. Hidden-states of the model at the output of each layer plus the
initial input to each Transformer encoder. The hidden states of the shallow encoders have length
`sequence_length`, but the hidden states of the deep encoder have length `sequence_length` //
`config.downsampling_rate`.
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 the 3 Transformer encoders of shape `(batch_size,
num_heads, sequence_length, sequence_length)` and `(batch_size, num_heads, sequence_length //
config.downsampling_rate, sequence_length // config.downsampling_rate)`. Attentions weights after the
attention softmax, used to compute the weighted average in the self-attention heads.
"""
last_hidden_state: torch.FloatTensor = None
pooler_output: torch.FloatTensor = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
def load_tf_weights_in_canine(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
# also discard the cls weights (which were used for the next sentence prediction pre-training task)
if any(
n
in [
"adam_v",
"adam_m",
"AdamWeightDecayOptimizer",
"AdamWeightDecayOptimizer_1",
"global_step",
"cls",
"autoregressive_decoder",
"char_output_weights",
]
for n in name
):
logger.info(f"Skipping {'/'.join(name)}")
continue
# if first scope name starts with "bert", change it to "encoder"
if name[0] == "bert":
name[0] = "encoder"
# remove "embeddings" middle name of HashBucketCodepointEmbedders
elif name[1] == "embeddings":
name.remove(name[1])
# rename segment_embeddings to token_type_embeddings
elif name[1] == "segment_embeddings":
name[1] = "token_type_embeddings"
# rename initial convolutional projection layer
elif name[1] == "initial_char_encoder":
name = ["chars_to_molecules"] + name[-2:]
# rename final convolutional projection layer
elif name[0] == "final_char_encoder" and name[1] in ["LayerNorm", "conv"]:
name = ["projection"] + name[1:]
pointer = model
for m_name in name:
if (re.fullmatch(r"[A-Za-z]+_\d+", m_name)) and "Embedder" not in 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")
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[-10:] in [f"Embedder_{i}" for i in range(8)]:
pointer = getattr(pointer, "weight")
elif m_name == "kernel":
array = np.transpose(array)
if pointer.shape != array.shape:
raise ValueError(f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched")
logger.info(f"Initialize PyTorch weight {name}")
pointer.data = torch.from_numpy(array)
return model
class CanineEmbeddings(nn.Module):
"""Construct the character, position and token_type embeddings."""
def __init__(self, config):
super().__init__()
self.config = config
# character embeddings
shard_embedding_size = config.hidden_size // config.num_hash_functions
for i in range(config.num_hash_functions):
name = f"HashBucketCodepointEmbedder_{i}"
setattr(self, name, nn.Embedding(config.num_hash_buckets, shard_embedding_size))
self.char_position_embeddings = nn.Embedding(config.num_hash_buckets, 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.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
def _hash_bucket_tensors(self, input_ids, num_hashes: int, num_buckets: int):
"""
Converts ids to hash bucket ids via multiple hashing.
Args:
input_ids: The codepoints or other IDs to be hashed.
num_hashes: The number of hash functions to use.
num_buckets: The number of hash buckets (i.e. embeddings in each table).
Returns:
A list of tensors, each of which is the hash bucket IDs from one hash function.
"""
if num_hashes > len(_PRIMES):
raise ValueError(f"`num_hashes` must be <= {len(_PRIMES)}")
primes = _PRIMES[:num_hashes]
result_tensors = []
for prime in primes:
hashed = ((input_ids + 1) * prime) % num_buckets
result_tensors.append(hashed)
return result_tensors
def _embed_hash_buckets(self, input_ids, embedding_size: int, num_hashes: int, num_buckets: int):
"""Converts IDs (e.g. codepoints) into embeddings via multiple hashing."""
if embedding_size % num_hashes != 0:
raise ValueError(f"Expected `embedding_size` ({embedding_size}) % `num_hashes` ({num_hashes}) == 0")
hash_bucket_tensors = self._hash_bucket_tensors(input_ids, num_hashes=num_hashes, num_buckets=num_buckets)
embedding_shards = []
for i, hash_bucket_ids in enumerate(hash_bucket_tensors):
name = f"HashBucketCodepointEmbedder_{i}"
shard_embeddings = getattr(self, name)(hash_bucket_ids)
embedding_shards.append(shard_embeddings)
return torch.cat(embedding_shards, dim=-1)
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,
) -> torch.FloatTensor:
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[:, :seq_length]
if token_type_ids is None:
token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
if inputs_embeds is None:
inputs_embeds = self._embed_hash_buckets(
input_ids, self.config.hidden_size, self.config.num_hash_functions, self.config.num_hash_buckets
)
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.char_position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class CharactersToMolecules(nn.Module):
"""Convert character sequence to initial molecule sequence (i.e. downsample) using strided convolutions."""
def __init__(self, config):
super().__init__()
self.conv = nn.Conv1d(
in_channels=config.hidden_size,
out_channels=config.hidden_size,
kernel_size=config.downsampling_rate,
stride=config.downsampling_rate,
)
self.activation = ACT2FN[config.hidden_act]
# 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)
def forward(self, char_encoding: torch.Tensor) -> torch.Tensor:
# `cls_encoding`: [batch, 1, hidden_size]
cls_encoding = char_encoding[:, 0:1, :]
# char_encoding has shape [batch, char_seq, hidden_size]
# We transpose it to be [batch, hidden_size, char_seq]
char_encoding = torch.transpose(char_encoding, 1, 2)
downsampled = self.conv(char_encoding)
downsampled = torch.transpose(downsampled, 1, 2)
downsampled = self.activation(downsampled)
# Truncate the last molecule in order to reserve a position for [CLS].
# Often, the last position is never used (unless we completely fill the
# text buffer). This is important in order to maintain alignment on TPUs
# (i.e. a multiple of 128).
downsampled_truncated = downsampled[:, 0:-1, :]
# We also keep [CLS] as a separate sequence position since we always
# want to reserve a position (and the model capacity that goes along
# with that) in the deep BERT stack.
# `result`: [batch, molecule_seq, molecule_dim]
result = torch.cat([cls_encoding, downsampled_truncated], dim=1)
result = self.LayerNorm(result)
return result
class ConvProjection(nn.Module):
"""
Project representations from hidden_size*2 back to hidden_size across a window of w = config.upsampling_kernel_size
characters.
"""
def __init__(self, config):
super().__init__()
self.config = config
self.conv = nn.Conv1d(
in_channels=config.hidden_size * 2,
out_channels=config.hidden_size,
kernel_size=config.upsampling_kernel_size,
stride=1,
)
self.activation = ACT2FN[config.hidden_act]
# 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)
def forward(
self,
inputs: torch.Tensor,
final_seq_char_positions: Optional[torch.Tensor] = None,
) -> torch.Tensor:
# inputs has shape [batch, mol_seq, molecule_hidden_size+char_hidden_final]
# we transpose it to be [batch, molecule_hidden_size+char_hidden_final, mol_seq]
inputs = torch.transpose(inputs, 1, 2)
# PyTorch < 1.9 does not support padding="same" (which is used in the original implementation),
# so we pad the tensor manually before passing it to the conv layer
# based on https://github.com/google-research/big_transfer/blob/49afe42338b62af9fbe18f0258197a33ee578a6b/bit_tf2/models.py#L36-L38
pad_total = self.config.upsampling_kernel_size - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
pad = nn.ConstantPad1d((pad_beg, pad_end), 0)
# `result`: shape (batch_size, char_seq_len, hidden_size)
result = self.conv(pad(inputs))
result = torch.transpose(result, 1, 2)
result = self.activation(result)
result = self.LayerNorm(result)
result = self.dropout(result)
final_char_seq = result
if final_seq_char_positions is not None:
# Limit transformer query seq and attention mask to these character
# positions to greatly reduce the compute cost. Typically, this is just
# done for the MLM training task.
# TODO add support for MLM
raise NotImplementedError("CanineForMaskedLM is currently not supported")
else:
query_seq = final_char_seq
return query_seq
class CanineSelfAttention(nn.Module):
def __init__(self, config):
super().__init__()
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
raise ValueError(
f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
f"heads ({config.num_attention_heads})"
)
self.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 = nn.Dropout(config.attention_probs_dropout_prob)
self.position_embedding_type = 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)
def transpose_for_scores(self, x):
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
x = x.view(*new_x_shape)
return x.permute(0, 2, 1, 3)
def forward(
self,
from_tensor: torch.Tensor,
to_tensor: torch.Tensor,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
mixed_query_layer = self.query(from_tensor)
# 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.
key_layer = self.transpose_for_scores(self.key(to_tensor))
value_layer = self.transpose_for_scores(self.value(to_tensor))
query_layer = self.transpose_for_scores(mixed_query_layer)
# 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":
seq_length = from_tensor.size()[1]
position_ids_l = torch.arange(seq_length, dtype=torch.long, device=from_tensor.device).view(-1, 1)
position_ids_r = torch.arange(seq_length, dtype=torch.long, device=from_tensor.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:
if attention_mask.ndim == 3:
# if attention_mask is 3D, do the following:
attention_mask = torch.unsqueeze(attention_mask, dim=1)
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and the dtype's smallest value for masked positions.
attention_mask = (1.0 - attention_mask.float()) * torch.finfo(attention_scores.dtype).min
# Apply the attention mask (precomputed for all layers in CanineModel forward() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.functional.softmax(attention_scores, 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,)
return outputs
class CanineSelfOutput(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: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor
) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class CanineAttention(nn.Module):
"""
Additional arguments related to local attention:
- **local** (`bool`, *optional*, defaults to `False`) -- Whether to apply local attention.
- **always_attend_to_first_position** (`bool`, *optional*, defaults to `False`) -- Should all blocks be able to
attend
to the `to_tensor`'s first position (e.g. a [CLS] position)? - **first_position_attends_to_all** (`bool`,
*optional*, defaults to `False`) -- Should the *from_tensor*'s first position be able to attend to all
positions within the *from_tensor*? - **attend_from_chunk_width** (`int`, *optional*, defaults to 128) -- The
width of each block-wise chunk in `from_tensor`. - **attend_from_chunk_stride** (`int`, *optional*, defaults to
128) -- The number of elements to skip when moving to the next block in `from_tensor`. -
**attend_to_chunk_width** (`int`, *optional*, defaults to 128) -- The width of each block-wise chunk in
*to_tensor*. - **attend_to_chunk_stride** (`int`, *optional*, defaults to 128) -- The number of elements to
skip when moving to the next block in `to_tensor`.
"""
def __init__(
self,
config,
local=False,
always_attend_to_first_position: bool = False,
first_position_attends_to_all: bool = False,
attend_from_chunk_width: int = 128,
attend_from_chunk_stride: int = 128,
attend_to_chunk_width: int = 128,
attend_to_chunk_stride: int = 128,
):
super().__init__()
self.self = CanineSelfAttention(config)
self.output = CanineSelfOutput(config)
self.pruned_heads = set()
# additional arguments related to local attention
self.local = local
if attend_from_chunk_width < attend_from_chunk_stride:
raise ValueError(
"`attend_from_chunk_width` < `attend_from_chunk_stride` would cause sequence positions to get skipped."
)
if attend_to_chunk_width < attend_to_chunk_stride:
raise ValueError(
"`attend_to_chunk_width` < `attend_to_chunk_stride`would cause sequence positions to get skipped."
)
self.always_attend_to_first_position = always_attend_to_first_position
self.first_position_attends_to_all = first_position_attends_to_all
self.attend_from_chunk_width = attend_from_chunk_width
self.attend_from_chunk_stride = attend_from_chunk_stride
self.attend_to_chunk_width = attend_to_chunk_width
self.attend_to_chunk_stride = attend_to_chunk_stride
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: Tuple[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
if not self.local:
self_outputs = self.self(hidden_states, hidden_states, attention_mask, head_mask, output_attentions)
attention_output = self_outputs[0]
else:
from_seq_length = to_seq_length = hidden_states.shape[1]
from_tensor = to_tensor = hidden_states
# Create chunks (windows) that we will attend *from* and then concatenate them.
from_chunks = []
if self.first_position_attends_to_all:
from_chunks.append((0, 1))
# We must skip this first position so that our output sequence is the
# correct length (this matters in the *from* sequence only).
from_start = 1
else:
from_start = 0
for chunk_start in range(from_start, from_seq_length, self.attend_from_chunk_stride):
chunk_end = min(from_seq_length, chunk_start + self.attend_from_chunk_width)
from_chunks.append((chunk_start, chunk_end))
# Determine the chunks (windows) that will will attend *to*.
to_chunks = []
if self.first_position_attends_to_all:
to_chunks.append((0, to_seq_length))
for chunk_start in range(0, to_seq_length, self.attend_to_chunk_stride):
chunk_end = min(to_seq_length, chunk_start + self.attend_to_chunk_width)
to_chunks.append((chunk_start, chunk_end))
if len(from_chunks) != len(to_chunks):
raise ValueError(
f"Expected to have same number of `from_chunks` ({from_chunks}) and "
f"`to_chunks` ({from_chunks}). Check strides."
)
# next, compute attention scores for each pair of windows and concatenate
attention_output_chunks = []
attention_probs_chunks = []
for (from_start, from_end), (to_start, to_end) in zip(from_chunks, to_chunks):
from_tensor_chunk = from_tensor[:, from_start:from_end, :]
to_tensor_chunk = to_tensor[:, to_start:to_end, :]
# `attention_mask`: <float>[batch_size, from_seq, to_seq]
# `attention_mask_chunk`: <float>[batch_size, from_seq_chunk, to_seq_chunk]
attention_mask_chunk = attention_mask[:, from_start:from_end, to_start:to_end]
if self.always_attend_to_first_position:
cls_attention_mask = attention_mask[:, from_start:from_end, 0:1]
attention_mask_chunk = torch.cat([cls_attention_mask, attention_mask_chunk], dim=2)
cls_position = to_tensor[:, 0:1, :]
to_tensor_chunk = torch.cat([cls_position, to_tensor_chunk], dim=1)
attention_outputs_chunk = self.self(
from_tensor_chunk, to_tensor_chunk, attention_mask_chunk, head_mask, output_attentions
)
attention_output_chunks.append(attention_outputs_chunk[0])
if output_attentions:
attention_probs_chunks.append(attention_outputs_chunk[1])
attention_output = torch.cat(attention_output_chunks, dim=1)
attention_output = self.output(attention_output, hidden_states)
outputs = (attention_output,)
if not self.local:
outputs = outputs + self_outputs[1:] # add attentions if we output them
else:
outputs = outputs + tuple(attention_probs_chunks) # add attentions if we output them
return outputs
class CanineIntermediate(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.FloatTensor) -> torch.FloatTensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class CanineOutput(nn.Module):
def __init__(self, config):
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: Tuple[torch.FloatTensor], input_tensor: torch.FloatTensor) -> torch.FloatTensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.LayerNorm(hidden_states + input_tensor)
return hidden_states
class CanineLayer(nn.Module):
def __init__(
self,
config,
local,
always_attend_to_first_position,
first_position_attends_to_all,
attend_from_chunk_width,
attend_from_chunk_stride,
attend_to_chunk_width,
attend_to_chunk_stride,
):
super().__init__()
self.chunk_size_feed_forward = config.chunk_size_feed_forward
self.seq_len_dim = 1
self.attention = CanineAttention(
config,
local,
always_attend_to_first_position,
first_position_attends_to_all,
attend_from_chunk_width,
attend_from_chunk_stride,
attend_to_chunk_width,
attend_to_chunk_stride,
)
self.intermediate = CanineIntermediate(config)
self.output = CanineOutput(config)
def forward(
self,
hidden_states: Tuple[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
self_attention_outputs = self.attention(
hidden_states,
attention_mask,
head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
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
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 CanineEncoder(nn.Module):
def __init__(
self,
config,
local=False,
always_attend_to_first_position=False,
first_position_attends_to_all=False,
attend_from_chunk_width=128,
attend_from_chunk_stride=128,
attend_to_chunk_width=128,
attend_to_chunk_stride=128,
):
super().__init__()
self.config = config
self.layer = nn.ModuleList(
[
CanineLayer(
config,
local,
always_attend_to_first_position,
first_position_attends_to_all,
attend_from_chunk_width,
attend_from_chunk_stride,
attend_to_chunk_width,
attend_to_chunk_stride,
)
for _ in range(config.num_hidden_layers)
]
)
self.gradient_checkpointing = False
def forward(
self,
hidden_states: Tuple[torch.FloatTensor],
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = False,
output_hidden_states: Optional[bool] = False,
return_dict: Optional[bool] = True,
) -> Union[Tuple, BaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions 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
if self.gradient_checkpointing and self.training:
def create_custom_forward(module):
def custom_forward(*inputs):
return module(*inputs, output_attentions)
return custom_forward
layer_outputs = torch.utils.checkpoint.checkpoint(
create_custom_forward(layer_module),
hidden_states,
attention_mask,
layer_head_mask,
)
else:
layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class CaninePooler(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: Tuple[torch.FloatTensor]) -> torch.FloatTensor:
# 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 CaninePredictionHeadTransform(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: Tuple[torch.FloatTensor]) -> torch.FloatTensor:
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class CanineLMPredictionHead(nn.Module):
def __init__(self, config):
super().__init__()
self.transform = CaninePredictionHeadTransform(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: Tuple[torch.FloatTensor]) -> torch.FloatTensor:
hidden_states = self.transform(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
class CanineOnlyMLMHead(nn.Module):
def __init__(self, config):
super().__init__()
self.predictions = CanineLMPredictionHead(config)
def forward(
self,
sequence_output: Tuple[torch.Tensor],
) -> Tuple[torch.Tensor]:
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class CaninePreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = CanineConfig
load_tf_weights = load_tf_weights_in_canine
base_model_prefix = "canine"
supports_gradient_checkpointing = True
_keys_to_ignore_on_load_missing = [r"position_ids"]
def _init_weights(self, module):
"""Initialize the weights"""
if isinstance(module, (nn.Linear, nn.Conv1d)):
# 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, CanineEncoder):
module.gradient_checkpointing = value
CANINE_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`CanineConfig`]): 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.
"""
CANINE_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 CANINE Model transformer outputting raw hidden-states without any specific head on top.",
CANINE_START_DOCSTRING,
)
class CanineModel(CaninePreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
shallow_config = copy.deepcopy(config)
shallow_config.num_hidden_layers = 1
self.char_embeddings = CanineEmbeddings(config)
# shallow/low-dim transformer encoder to get a initial character encoding