e5-base-v2-4096 / modeling_lsg_bert.py
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from logging import warn
from transformers.models.bert.modeling_bert import *
import torch
import torch.nn as nn
from transformers.models.bert.configuration_bert import BertConfig
import sys
AUTO_MAP = {
"AutoModel": "modeling_lsg_bert.LSGBertModel",
"AutoModelForCausalLM": "modeling_lsg_bert.LSGBertLMHeadModel",
"AutoModelForMaskedLM": "modeling_lsg_bert.LSGBertForMaskedLM",
"AutoModelForPreTraining": "modeling_lsg_bert.LSGBertForPreTraining",
"AutoModelForMultipleChoice": "modeling_lsg_bert.LSGBertForMultipleChoice",
"AutoModelForQuestionAnswering": "modeling_lsg_bert.LSGBertForQuestionAnswering",
"AutoModelForSequenceClassification": "modeling_lsg_bert.LSGBertForSequenceClassification",
"AutoModelForTokenClassification": "modeling_lsg_bert.LSGBertForTokenClassification"
}
class LSGBertConfig(BertConfig):
"""
This class overrides :class:`~transformers.BertConfig`. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
base_model_prefix = "lsg"
model_type = "bert"
def __init__(
self,
adaptive=True,
base_model_prefix="lsg",
block_size=128,
lsh_num_pre_rounds=1,
mask_first_token=False,
num_global_tokens=1,
pool_with_global=True,
sparse_block_size=128,
sparsity_factor=2,
sparsity_type="norm",
**kwargs
):
"""Constructs LSGBertConfig."""
super().__init__(**kwargs)
self.adaptive = adaptive
self.auto_map = AUTO_MAP
self.base_model_prefix = base_model_prefix
self.block_size = block_size
self.lsh_num_pre_rounds = lsh_num_pre_rounds
self.mask_first_token = mask_first_token
self.num_global_tokens = num_global_tokens
self.pool_with_global = pool_with_global
self.sparse_block_size = sparse_block_size
self.sparsity_factor = sparsity_factor
self.sparsity_type = sparsity_type
if sparsity_type not in [None, "none", "norm", "lsh", "pooling", "stride", "block_stride"]:
logger.warning(
"[WARNING CONFIG]: sparsity_mode not in [None, 'none', 'norm', 'lsh', 'pooling', 'stride', 'block_stride'], \
setting sparsity_type=None, computation will skip sparse attention")
self.sparsity_type = None
if self.sparsity_type in ["stride", "block_stride"]:
if self.sparsity_factor > self.encoder_attention_heads:
logger.warning(
"[WARNING CONFIG]: sparsity_factor > encoder_attention_heads is not recommended for stride/block_stride sparsity"
)
if self.num_global_tokens < 1:
logger.warning(
"[WARNING CONFIG]: num_global_tokens < 1 is not compatible, setting num_global_tokens=1"
)
self.num_global_tokens = 1
elif self.num_global_tokens > 512:
logger.warning(
"[WARNING CONFIG]: num_global_tokens > 512 is not allowed, setting num_global_tokens=512"
)
self.num_global_tokens = 512
if self.sparsity_factor > 0:
assert self.block_size % self.sparsity_factor == 0, "[ERROR CONFIG]: block_size must be divisible by sparsity_factor"
assert self.block_size//self.sparsity_factor >= 1, "[ERROR CONFIG]: make sure block_size >= sparsity_factor"
if self.mask_first_token and not pool_with_global:
logger.warning(
"[WARNING CONFIG]: pool_with_global==False is not compatible with mask_first_token==True. Setting pool_with_global to True.")
self.pool_with_global = True
if hasattr(self, "position_embedding_type"):
if self.position_embedding_type != "absolute":
logger.warning(
"[WARNING CONFIG]: LSG Attention is not compatible with relative positional embedding and will skip its computation. Set position_embedding_type='absolute' to remove this warning.")
class BaseSelfAttention(nn.Module):
def init_modules(self, config):
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(
config, "embedding_size"
):
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, 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)
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 reshape_output(self, context_layer):
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
return context_layer.view(*new_context_layer_shape)
def project_QKV(self, hidden_states):
query_layer = self.transpose_for_scores(self.query(hidden_states))
key_layer = self.transpose_for_scores(self.key(hidden_states))
value_layer = self.transpose_for_scores(self.value(hidden_states))
return query_layer, key_layer, value_layer
class BaseAttentionProduct(nn.Module):
def __init__(self, config):
"""
Compute attention: softmax(Q @ K.T) @ V
"""
super().__init__()
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
def forward(self, query_layer, key_layer, value_layer, attention_mask=None):
d = query_layer.shape[-1]
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = query_layer @ key_layer.transpose(-1, -2) / math.sqrt(d)
del query_layer
del key_layer
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
del attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
context_layer = self.dropout(attention_probs) @ value_layer
return context_layer
class CausalAttentionProduct(nn.Module):
def __init__(self, config):
"""
Compute attention: softmax(Q @ K.T) @ V
"""
super().__init__()
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
self.block_size = config.block_size
def forward(self, query_layer, key_layer, value_layer, attention_mask=None, causal_shape=None):
d = query_layer.shape[-1]
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = query_layer @ key_layer.transpose(-1, -2) / math.sqrt(d)
del query_layer
del key_layer
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
# Add causal mask
causal_shape = (self.block_size, self.block_size) if causal_shape is None else causal_shape
causal_mask = torch.tril(
torch.ones(*causal_shape, device=attention_mask.device, dtype=attention_scores.dtype),
diagonal=-1
)
causal_mask = causal_mask.T * torch.finfo(attention_scores.dtype).min
attention_scores[..., -causal_shape[0]:, -causal_shape[1] + 1:] = causal_mask[:, 1:]
del attention_mask
# Normalize the attention scores to probabilities.
attention_probs = nn.Softmax(dim=-1)(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
context_layer = self.dropout(attention_probs) @ value_layer
return context_layer
class LSGAttentionProduct(nn.Module):
def __init__(self, config, block_size=None, sparse_block_size=None, sparsity_factor=4, is_causal=False):
"""
Compute block or overlapping blocks attention products
"""
super().__init__()
self.block_size = block_size
self.sparse_block_size = sparse_block_size
self.sparsity_factor = sparsity_factor
self.is_causal = is_causal
if self.block_size is None:
self.block_size = config.block_size
if self.sparse_block_size is None:
self.sparse_block_size = config.sparse_block_size
# Shape of blocks
self.local_shapes = (self.block_size*3, self.block_size)
if self.sparse_block_size and self.sparsity_factor > 0:
self.sparse_shapes = (self.sparse_block_size*3, self.block_size//self.sparsity_factor)
if is_causal:
self.attention = CausalAttentionProduct(config)
else:
self.attention = BaseAttentionProduct(config)
def build_lsg_inputs(self, hidden_states, sparse_hidden_states, global_hidden_states, is_attn_mask=False):
# Build local tokens
local_hidden_states = self.reshape_to_local_block(hidden_states, is_attn_mask)
del hidden_states
# Build sparse tokens
if sparse_hidden_states is not None:
sparse_hidden_states = self.reshape_to_sparse_block(sparse_hidden_states, is_attn_mask)
return self.cat_global_sparse_local_tokens(global_hidden_states, sparse_hidden_states, local_hidden_states)
def forward(
self,
query_layer,
key_layer,
value_layer,
attention_mask=None,
sparse_key=None,
sparse_value=None,
sparse_mask=None,
global_key=None,
global_value=None,
global_mask=None
):
# Input batch, heads, length, hidden_size
n, h, t, d = query_layer.size()
n_blocks = t // self.block_size
assert t % self.block_size == 0
key_layer = self.build_lsg_inputs(
key_layer,
sparse_key,
global_key
)
del sparse_key
del global_key
value_layer = self.build_lsg_inputs(
value_layer,
sparse_value,
global_value
)
del sparse_value
del global_value
attention_mask = self.build_lsg_inputs(
attention_mask,
sparse_mask,
global_mask.transpose(-1, -2),
is_attn_mask=True
).transpose(-1, -2)
del sparse_mask
del global_mask
# expect (..., t, d) shape
# Compute attention
context_layer = self.attention(
query_layer=self.chunk(query_layer, n_blocks),
key_layer=key_layer,
value_layer=value_layer,
attention_mask=attention_mask
)
return context_layer.reshape(n, h, -1, d)
def reshape_to_local_block(self, hidden_states, is_attn_mask=False):
size, step = self.local_shapes
s = (size - step) // 2
# Pad before block reshaping
if is_attn_mask:
pad_value = torch.finfo(hidden_states.dtype).min
hidden_states = hidden_states.transpose(-1, -2)
else:
pad_value = 0
hidden_states = torch.nn.functional.pad(
hidden_states.transpose(-1, -2),
pad=(s, s),
value=pad_value
).transpose(-1, -2)
# Make blocks
hidden_states = hidden_states.unfold(-2, size=size, step=step).transpose(-1, -2)
# Skip third block if causal
if self.is_causal:
return hidden_states[..., :size*2//3, :]
return hidden_states
def reshape_to_sparse_block(self, hidden_states, is_attn_mask=False):
size, step = self.sparse_shapes
# In case of odd case
odd_offset = (step % 2)
# n, h, t, d*2 + 1
size = size*2
s = (size - step) // 2 + odd_offset
# Pad before block reshaping
if is_attn_mask:
pad_value = torch.finfo(hidden_states.dtype).min
hidden_states = hidden_states.transpose(-1, -2)
else:
pad_value = 0
hidden_states = torch.nn.functional.pad(
hidden_states.transpose(-1, -2),
pad=(s, s),
value=pad_value
).transpose(-1, -2)
# Make blocks
hidden_states = hidden_states.unfold(-2, size=size, step=step).transpose(-1, -2)
# Fix case where block_size == sparsify_factor
if odd_offset:
hidden_states = hidden_states[..., :-1, :, :]
# Indexes for selection
u = (size - self.block_size * 3 // self.sparsity_factor) // 2 + odd_offset
s = self.sparse_block_size
# Skip right block if causal
if self.is_causal:
return hidden_states[..., u-s:u, :]
u_ = u + odd_offset
return torch.cat([hidden_states[..., u-s:u, :], hidden_states[..., -u_:-u_+s, :]], dim=-2)
def cat_global_sparse_local_tokens(self, x_global, x_sparse=None, x_local=None, dim=-2):
n, h, b, t, d = x_local.size()
x_global = x_global.unsqueeze(-3).expand(-1, -1, b, -1, -1)
if x_sparse is not None:
return torch.cat([x_global, x_sparse, x_local], dim=dim)
return torch.cat([x_global, x_local], dim=dim)
def chunk(self, x, n_blocks):
t, d = x.size()[-2:]
return x.reshape(*x.size()[:-2], n_blocks, -1, d)
class LSGBertEmbeddings(BertEmbeddings):
def __init__(self, config):
super().__init__(config)
self.num_global_tokens = config.num_global_tokens
# Hardcoded but partially trained
self.global_embeddings = nn.Embedding(512, embedding_dim=config.hidden_size, )
self.block_size = config.block_size
def forward(
self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None, past_key_values_length=0
):
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[:, :seq_length])
embeddings = inputs_embeds + token_type_embeddings
if self.position_embedding_type == "absolute":
position_embeddings = self.position_embeddings(position_ids[:, :seq_length])
embeddings += position_embeddings
#if self.num_global_tokens < 0:
n, t, d = embeddings.size()
# Add global_tokens
indexes = torch.arange(self.num_global_tokens, device=embeddings.device).reshape(1, -1)
global_embeddings = self.global_embeddings(indexes)
embeddings = torch.cat([global_embeddings.expand(n, -1, d), embeddings], dim=-2)
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class LSGSelfAttention(BaseSelfAttention):
'''
Compute local attention with overlapping blocs
Use global attention for tokens with highest norm
'''
def __init__(self, config):
super().__init__()
self.init_modules(config)
self.block_size = config.block_size
self.sparse_block_size = config.sparse_block_size
self.num_global_tokens = config.num_global_tokens
self.sparsity_factor = config.sparsity_factor
self.is_causal = config.is_decoder
self.is_decoder = config.is_decoder
self.attention = LSGAttentionProduct(
config,
block_size=config.block_size,
sparse_block_size=config.sparse_block_size,
sparsity_factor=self.sparsity_factor,
is_causal=self.is_causal
)
if self.is_causal:
self.causal_attention = CausalAttentionProduct(config)
self.full_attention = BaseAttentionProduct(config)
sparse_functions = {
"norm": self.get_sparse_tokens_with_norm,
"pooling": self.get_sparse_tokens_with_pooling,
"lsh": self.get_sparse_tokens_with_lsh,
"stride": self.get_sparse_tokens_with_stride,
"block_stride": self.get_sparse_tokens_with_block_stride,
}
self.sparsity_type = config.sparsity_type
self.get_sparse_elements = sparse_functions.get(self.sparsity_type, lambda x, y, z: (None, None, None))
if config.sparsity_type == "lsh":
self.lsh_num_pre_rounds = config.lsh_num_pre_rounds
def get_sparse_tokens_with_norm(self, keys, values, mask):
if self.sparsity_factor == 1:
return keys, values, mask.expand(-1, keys.size()[1], -1, -1)
with torch.no_grad():
block_size = min(self.block_size, self.sparse_block_size)
key_norm = keys.detach().norm(dim=-1, keepdim=True)
key_norm = key_norm * ~mask.transpose(-1, -2).bool()
key_norm = self.chunk(key_norm, block_size)
n, h, b, t, d = key_norm.size()
idx = key_norm.argsort(dim=-2)
del key_norm
idx += (torch.arange(b, device=keys.device)*t).reshape(1, 1, b, 1, 1)
split = (t - block_size // self.sparsity_factor, block_size // self.sparsity_factor)
sparse_idx = idx.split(split, -2)[-1].reshape(n, h, -1, 1)
d = keys.size()[-1]
keys = keys.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d))
values = values.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d))
mask = mask.expand(-1, h, -1, -1).transpose(-1, -2).gather(dim=-2, index=sparse_idx).transpose(-1, -2)
return keys, values, mask
def get_sparse_tokens_with_pooling(self, keys, values, mask):
if self.sparsity_factor == 1:
return keys, values, mask.expand(-1, keys.size()[1], -1, -1)
keys = self.chunk(keys, self.sparsity_factor)
values = self.chunk(values, self.sparsity_factor)
n, h, b, t, d = keys.size()
mask = mask.reshape(n, 1, b, 1, t)
mask = ~mask.transpose(-1, -2).bool()
keys = keys * mask
values = values * mask
mask = mask.sum(dim=-2)
keys = keys.sum(dim=-2) / (mask + 1e-6)
values = values.sum(dim=-2) / (mask + 1e-6)
mask = (1. - mask.clamp(0, 1))
mask *= torch.finfo(mask.dtype).min
return keys.reshape(n, h, -1, d), values.reshape(n, h, -1, d), mask.expand(-1, h, -1, -1).transpose(-1, -2)
def get_sparse_tokens_with_stride(self, keys, values, mask):
if self.sparsity_factor == 1:
return keys, values, mask.expand(-1, keys.size()[1], -1, -1)
n, h, t, d = keys.size()
sparse_idx = torch.arange(t // self.sparsity_factor, device=keys.device) * self.sparsity_factor
sparse_idx = sparse_idx.reshape(1, 1, -1, 1) + (torch.arange(h, device=keys.device) % self.sparsity_factor).reshape(1, h, 1, 1)
sparse_idx = sparse_idx.expand(n, h, -1, 1)
keys = keys.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d))
values = values.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d))
mask = mask.expand(-1, h, -1, -1).transpose(-1, -2).gather(dim=-2, index=sparse_idx).transpose(-1, -2)
return keys, values, mask
def get_sparse_tokens_with_block_stride(self, keys, values, mask):
if self.sparsity_factor == 1:
return keys, values, mask.expand(-1, keys.size()[1], -1, -1)
n, h, t, d = keys.size()
t, b = self.block_size, t // self.block_size
sparse_idx = torch.arange(t // self.sparsity_factor, device=keys.device)
sparse_idx = sparse_idx.reshape(1, 1, 1, -1, 1) + torch.arange(h, device=keys.device).reshape(1, h, 1, 1, 1) * (t // self.sparsity_factor)
sparse_idx = (sparse_idx % t)
sparse_idx = sparse_idx + torch.arange(b, device=keys.device).reshape(1, 1, -1, 1, 1) * t
sparse_idx = sparse_idx.reshape(1, h, -1, 1).expand(n, h, -1, 1)
keys = keys.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d))
values = values.gather(dim=-2, index=sparse_idx.expand(-1, -1, -1, d))
mask = mask.expand(-1, h, -1, -1).transpose(-1, -2).gather(dim=-2, index=sparse_idx).transpose(-1, -2)
return keys, values, mask
def get_sparse_tokens_with_lsh(self, keys, values, mask):
if self.sparsity_factor == 1:
return keys, values, mask.expand(-1, keys.size()[1], -1, -1)
block_size = min(self.block_size, self.sparse_block_size)
keys = self.chunk(keys, block_size)
values = self.chunk(values, block_size)
n, h, b, t, d = keys.size()
mask = mask.reshape(n, 1, b, 1, t)
mask = ~mask.transpose(-1, -2).bool()
keys = keys * mask
values = values * mask
mask = mask.expand(-1, h, -1, -1, -1).float()
extra_factor = 1
for _ in range(self.lsh_num_pre_rounds):
keys, values, mask = self.lsh_round(keys, values, mask, t*extra_factor)
keys, values, mask = self.lsh_round(keys, values, mask, t//self.sparsity_factor)
keys /= mask + 1e-8
values /= mask + 1e-8
mask = (1. - mask.clamp(0, 1))
mask *= torch.finfo(mask.dtype).min
return keys.reshape(n, h, -1, d), values.reshape(n, h, -1, d), mask.transpose(-1, -2).reshape(n, h, 1, -1)
def lsh_round(self, keys, values, mask, output_size):
with torch.no_grad():
n_hashes = output_size // 2
n, h, b, t, d = keys.size()
binary_mask = mask.clamp(0, 1)
indexes = (torch.nn.functional.normalize(keys, dim=-1) * binary_mask) @ torch.randn(1, h, 1, d, n_hashes, device=keys.device)
indexes = torch.cat([indexes, -indexes], dim=-1).argmax(dim=-1, keepdim=True)
n, h, b, t, d = keys.size()
x_ = torch.zeros(n, h, b, output_size, d, device=keys.device)
mask_ = torch.zeros(n, h, b, output_size, 1, device=keys.device)
keys = torch.scatter_add(x_, dim=-2, index=indexes.expand(-1, -1, -1, -1, d), src=keys)
values = torch.scatter_add(x_, dim=-2, index=indexes.expand(-1, -1, -1, -1, d), src=values)
mask = torch.scatter_add(mask_, dim=-2, index=indexes, src=mask)
return keys[..., :output_size, :], values[..., :output_size, :], mask[..., :output_size, :]
def forward(
self,
hidden_states,
attention_mask=None,
head_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_value=None,
output_attentions=False,
):
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(query_layer)
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 is_cross_attention:
outputs = self.cross_attention_forward(
query_layer=query_layer,
key_layer=key_layer,
value_layer=value_layer,
attention_mask=attention_mask,
output_attentions=output_attentions
)
else:
outputs = self.causal_forward(
query_layer,
key_layer,
value_layer,
attention_mask=attention_mask,
output_attentions=output_attentions,
)
outputs = outputs + ((key_layer, value_layer),)
else:
outputs = self.not_causal_forward(
query_layer,
key_layer,
value_layer,
attention_mask=attention_mask,
output_attentions=output_attentions
)
return outputs
def causal_forward(
self,
query_layer,
key_layer,
value_layer,
attention_mask=None,
output_attentions=False,
):
n, h, t, d = key_layer.size()
# Cat global mask
attention_mask = torch.nn.functional.pad(attention_mask, (self.num_global_tokens, 0), value=0)
# Split input into global tokens and other tokens
split = (self.num_global_tokens, t - self.num_global_tokens)
global_query, query_layer = query_layer.split(split, dim=-2)
# Use normal causal attention if local attention covers every tokens
if t <= 2 * self.block_size + self.num_global_tokens:
context_layer = self.causal_attention(
query_layer=query_layer,
key_layer=key_layer,
value_layer=value_layer,
attention_mask=attention_mask,
causal_shape=(t - self.num_global_tokens, t - self.num_global_tokens)
)
context_layer = torch.cat([global_query, context_layer], dim=-2)
return (self.reshape_output(context_layer), )
# Split K Q M on global and non global
global_key, key_layer = key_layer.split(split, dim=-2)
global_value, value_layer = value_layer.split(split, dim=-2)
global_mask, attention_mask = attention_mask.split(split, dim=-1)
n, h, t, d = key_layer.size()
# Get sparse idx
sparse_key, sparse_value, sparse_mask = (None, None, None)
if self.sparse_block_size and self.sparsity_factor > 0:
sparse_key, sparse_value, sparse_mask = self.get_sparse_elements(key_layer, value_layer, attention_mask)
# Expand masks on heads
attention_mask = attention_mask.expand(-1, h, -1, -1)
global_mask = global_mask.expand(-1, h, -1, -1)
# Compute dot product attention
context_layer = self.attention(
query_layer,
key_layer,
value_layer,
attention_mask,
sparse_key=sparse_key,
sparse_value=sparse_value,
sparse_mask=sparse_mask,
global_key=global_key,
global_value=global_value,
global_mask=global_mask
)
# Merge pseudo global (causal) and local-sparse tokens
context_layer = torch.cat([global_query, context_layer], dim=-2)
context_layer = self.reshape_output(context_layer)
return (context_layer,)
def not_causal_forward(
self,
query_layer,
key_layer,
value_layer,
attention_mask=None,
output_attentions=False,
):
n, h, t, d = query_layer.size()
# Cat global mask
attention_mask = torch.nn.functional.pad(attention_mask, (self.num_global_tokens, 0), value=0)
# Use normal attention if local attention covers every tokens
if t <= 2 * self.block_size + self.num_global_tokens:
context_layer = self.full_attention(
query_layer=query_layer,
key_layer=key_layer,
value_layer=value_layer,
attention_mask=attention_mask
)
return (self.reshape_output(context_layer), )
# Split input into global tokens and other tokens
split = (self.num_global_tokens, t - self.num_global_tokens)
global_query, query_layer = query_layer.split(split, dim=-2)
# Get global_attention
bos = self.full_attention(
query_layer=global_query,
key_layer=key_layer,
value_layer=value_layer,
attention_mask=attention_mask
)
# Split K Q M on global and non global
global_key, key_layer = key_layer.split(split, dim=-2)
global_value, value_layer = value_layer.split(split, dim=-2)
global_mask, attention_mask = attention_mask.split(split, dim=-1)
n, h, t, d = key_layer.size()
# Get sparse idx
sparse_key, sparse_value, sparse_mask = (None, None, None)
if self.sparse_block_size and self.sparsity_factor > 0:
sparse_key, sparse_value, sparse_mask = self.get_sparse_elements(key_layer, value_layer, attention_mask)
# Expand masks on heads
attention_mask = attention_mask.expand(-1, h, -1, -1)
global_mask = global_mask.expand(-1, h, -1, -1)
# Compute dot product attention
context_layer = self.attention(
query_layer,
key_layer,
value_layer,
attention_mask,
sparse_key=sparse_key,
sparse_value=sparse_value,
sparse_mask=sparse_mask,
global_key=global_key,
global_value=global_value,
global_mask=global_mask
)
# Merge global and local-sparse tokens
context_layer = torch.cat([bos, context_layer], dim=-2)
context_layer = self.reshape_output(context_layer)
return (context_layer,)
def cross_attention_forward(
self,
query_layer,
key_layer,
value_layer,
attention_mask=None,
output_attentions=False,
):
context_layer = self.full_attention(
query_layer=query_layer,
key_layer=key_layer,
value_layer=value_layer,
attention_mask=attention_mask
)
return (self.reshape_output(context_layer), )
def chunk(self, x, chunk_size):
n, h, t, d = x.size()
return x.reshape(n, h, -1, chunk_size, d)
class LSGAttention(BertAttention):
def __init__(self, config):
nn.Module.__init__(self)
self.self = LSGSelfAttention(config)
self.output = BertSelfOutput(config)
self.pruned_heads = set()
class LSGBertLayer(BertLayer):
def __init__(self, config):
super().__init__(config)
self.attention = LSGAttention(config)
if self.add_cross_attention:
if not self.is_decoder:
assert self.is_decoder, f"{self} should be used as a decoder model if cross attention is added"
self.crossattention = LSGAttention(config)
class LSGBertEncoder(BertEncoder):
def __init__(self, config):
super().__init__(config)
self.layer = nn.ModuleList([LSGBertLayer(config) for _ in range(config.num_hidden_layers)])
assert hasattr(config, "num_global_tokens")
self.num_global_tokens = config.num_global_tokens
self.pad_idx = config.pad_token_id
assert hasattr(config, "block_size") and hasattr(config, "adaptive")
self.block_size = config.block_size
self.adaptive = config.adaptive
self.mask_first_token = config.mask_first_token
self.pool_with_global = config.pool_with_global
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]:
mask_value = torch.finfo(attention_mask.dtype).min
n, _, __, t = attention_mask.size()
if not (self.config.is_decoder and encoder_hidden_states is not None):
b = self.block_size * 2
pad = t % self.block_size
# Check if t is multiple of block_size and pad
if self.adaptive and t > b and pad > 0:
pad_length = self.block_size - pad
hidden_states = torch.nn.functional.pad(hidden_states.transpose(-1, -2), (0, pad_length), value=0.).transpose(-1, -2)
attention_mask = torch.nn.functional.pad(attention_mask, (0, pad_length), value=mask_value)
if self.mask_first_token:
attention_mask[..., 0] = mask_value
encoder_outputs = super().forward(
hidden_states=hidden_states,
attention_mask=attention_mask,
head_mask=head_mask,
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 = encoder_outputs[0]
if self.pool_with_global:
sequence_output[:, self.num_global_tokens] = sequence_output[:, 0]
# Adapt sequence to initial shape
sequence_output = sequence_output[..., self.num_global_tokens: t + self.num_global_tokens, :]
if not return_dict:
return (sequence_output, ) + encoder_outputs[1:]
encoder_outputs.last_hidden_state = sequence_output
return encoder_outputs
class LSGBertPreTrainedModel(BertPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = LSGBertConfig
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, (BertEncoder, LSGBertEncoder)):
module.gradient_checkpointing = value
class LSGBertModel(LSGBertPreTrainedModel, BertModel):
"""
This class overrides :class:`~transformers.BertModel`. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
config_class = LSGBertConfig
def __init__(self, config, add_pooling_layer=True):
LSGBertPreTrainedModel.__init__(self, config)
self.config = config
self.embeddings = LSGBertEmbeddings(config)
self.encoder = LSGBertEncoder(config)
self.pooler = BertPooler(config) if add_pooling_layer else None
if config.add_cross_attention:
logger.warning(
"Cross attention is computed using full attention since it is not LSG compatible."
)
# Initialize weights and apply final processing
self.post_init()
def get_extended_attention_mask(self, attention_mask, input_shape, device=None):
# Do not rely on original triangular mask from BERT/RoBERTa for causalLM
if attention_mask.dim() == 3:
extended_attention_mask = attention_mask[:, None, :, :]
elif attention_mask.dim() == 2:
extended_attention_mask = attention_mask[:, None, None, :]
else:
raise ValueError(
f"Wrong shape for input_ids (shape {input_shape}) or attention_mask (shape {attention_mask.shape})"
)
extended_attention_mask = extended_attention_mask.to(dtype=self.dtype) # fp16 compatibility
extended_attention_mask = (1.0 - extended_attention_mask) * torch.finfo(extended_attention_mask.dtype).min
return extended_attention_mask
class LSGBertForPreTraining(LSGBertPreTrainedModel, BertForPreTraining):
def __init__(self, config):
LSGBertPreTrainedModel.__init__(self, config)
self.bert = LSGBertModel(config)
self.cls = BertPreTrainingHeads(config)
# Initialize weights and apply final processing
self.post_init()
class LSGBertLMHeadModel(LSGBertPreTrainedModel, BertLMHeadModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
def __init__(self, config):
LSGBertPreTrainedModel.__init__(self, config)
if not config.is_decoder:
logger.warning("If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`")
self.bert = LSGBertModel(config, add_pooling_layer=False)
self.cls = BertOnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
class LSGBertForMaskedLM(LSGBertPreTrainedModel, BertForMaskedLM):
"""
This class overrides :class:`~transformers.BertForMaskedLM`. Please check the superclass for the appropriate
documentation alongside usage examples.
"""
config_class = LSGBertConfig
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
def __init__(self, config):
LSGBertPreTrainedModel.__init__(self, config)
if config.is_decoder:
logger.warning(
"If you want to use `LSGBertForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.bert = LSGBertModel(config, add_pooling_layer=False)
self.cls = BertOnlyMLMHead(config)
# Initialize weights and apply final processing
self.post_init()
class LSGBertForNextSentencePrediction(LSGBertPreTrainedModel, BertForNextSentencePrediction):
def __init__(self, config):
LSGBertPreTrainedModel.__init__(self, config)
self.bert = LSGBertModel(config)
self.cls = BertOnlyNSPHead(config)
# Initialize weights and apply final processing
self.post_init()
class LSGBertForSequenceClassification(LSGBertPreTrainedModel, BertForSequenceClassification):
"""
This class overrides :class:`~transformers.BertForSequenceClassification`. Please check the superclass for the
appropriate documentation alongside usage examples.
"""
config_class = LSGBertConfig
def __init__(self, config):
LSGBertPreTrainedModel.__init__(self, config)
self.num_labels = config.num_labels
self.config = config
self.bert = LSGBertModel(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()
class LSGBertForMultipleChoice(LSGBertPreTrainedModel, BertForMultipleChoice):
"""
This class overrides :class:`~transformers.BertForMultipleChoice`. Please check the superclass for the
appropriate documentation alongside usage examples.
"""
config_class = LSGBertConfig
def __init__(self, config):
LSGBertPreTrainedModel.__init__(self, config)
self.bert = LSGBertModel(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()
class LSGBertForTokenClassification(LSGBertPreTrainedModel, BertForTokenClassification):
"""
This class overrides :class:`~transformers.BertForTokenClassification`. Please check the superclass for the
appropriate documentation alongside usage examples.
"""
config_class = LSGBertConfig
_keys_to_ignore_on_load_unexpected = [r"pooler"]
def __init__(self, config):
LSGBertPreTrainedModel.__init__(self, config)
self.num_labels = config.num_labels
self.bert = LSGBertModel(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()
class LSGBertForQuestionAnswering(LSGBertPreTrainedModel, BertForQuestionAnswering):
"""
This class overrides :class:`~transformers.BertForQuestionAnswering`. Please check the superclass for the
appropriate documentation alongside usage examples.
"""
config_class = LSGBertConfig
_keys_to_ignore_on_load_unexpected = [r"pooler"]
def __init__(self, config):
LSGBertPreTrainedModel.__init__(self, config)
self.num_labels = config.num_labels
self.bert = LSGBertModel(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()
def str_to_class(classname):
return getattr(sys.modules[__name__], classname)
# Register model in Auto API
try:
LSGBertConfig.register_for_auto_class()
for key, value in AUTO_MAP.items():
str_to_class(value.split(".")[-1]).register_for_auto_class(key)
except:
warn("AutoRegister isn't available, you'll have to manually copy modeling.py after .save_pretrained(...).")
warn("Update to transformers >= 4.23.1 to fix.")