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# modeling_mrt5.py
# Author: Julie Kallini
# Description: This file contains the implementation of the MrT5 model.
# The code is adapted from HuggingFace's modeling_t5.py. New code sequences
# are labeled with comments.
import torch
import copy
import numpy as np
from torch import nn
from .modeling_t5 import (
T5Attention,
T5LayerNorm,
T5LayerFF,
T5Stack,
T5ForConditionalGeneration,
softmax1,
)
from .configuration_mrt5 import MrT5Config
from transformers.modeling_outputs import (
BaseModelOutput,
BaseModelOutputWithPastAndCrossAttentions,
Seq2SeqLMOutput,
)
from transformers.utils import logging
from typing import Optional, Tuple, Union
from dataclasses import dataclass
logger = logging.get_logger(__name__)
@dataclass
class MrT5BaseModelOutputWithPastAndCrossAttentions(BaseModelOutputWithPastAndCrossAttentions):
delete_gate_mask: torch.FloatTensor = None
delete_gate_output: torch.FloatTensor = None
delete_gate_logits: torch.FloatTensor = None
attention_mask: torch.FloatTensor = None
attention_queries: torch.FloatTensor = None
attention_keys: torch.FloatTensor = None
attention_values: torch.FloatTensor = None
attention_scores: torch.FloatTensor = None
cross_attention_keys: torch.FloatTensor = None
cross_attention_queries: torch.FloatTensor = None
cross_attention_values: torch.FloatTensor = None
cross_attention_scores: torch.FloatTensor = None
@dataclass
class MrT5Seq2SeqLMOutput(Seq2SeqLMOutput):
delete_gate_mask: torch.FloatTensor = None
delete_gate_output: torch.FloatTensor = None
delete_gate_logits: torch.FloatTensor = None
encoder_keys: torch.FloatTensor = None
encoder_queries: torch.FloatTensor = None
encoder_values: torch.FloatTensor = None
encoder_scores: torch.FloatTensor = None
decoder_keys: torch.FloatTensor = None
decoder_queries: torch.FloatTensor = None
decoder_values: torch.FloatTensor = None
decoder_scores: torch.FloatTensor = None
cross_attention_keys: torch.FloatTensor = None
cross_attention_queries: torch.FloatTensor = None
cross_attention_values: torch.FloatTensor = None
cross_attention_scores: torch.FloatTensor = None
TORCH_INIT_FUNCTIONS = {
"uniform_": nn.init.uniform_,
"normal_": nn.init.normal_,
"trunc_normal_": nn.init.trunc_normal_,
"constant_": nn.init.constant_,
"xavier_uniform_": nn.init.xavier_uniform_,
"xavier_normal_": nn.init.xavier_normal_,
"kaiming_uniform_": nn.init.kaiming_uniform_,
"kaiming_normal_": nn.init.kaiming_normal_,
"uniform": nn.init.uniform,
"normal": nn.init.normal,
"xavier_uniform": nn.init.xavier_uniform,
"xavier_normal": nn.init.xavier_normal,
"kaiming_uniform": nn.init.kaiming_uniform,
"kaiming_normal": nn.init.kaiming_normal,
}
class ScaledSigmoid(nn.Module):
def __init__(self, sigmoid_mask_scale):
super().__init__()
self.sigmoid_mask_scale = sigmoid_mask_scale
def forward(self, input):
return self.sigmoid_mask_scale * torch.sigmoid(-input)
def gumbel_noise_like(x: torch.Tensor) -> torch.Tensor:
eps = 3e-4 if x.dtype == torch.float16 else 1e-10
uniform = torch.empty_like(x).uniform_(eps, 1 - eps)
return - (- uniform.log()).log()
class SigmoidDeleteGate(nn.Module):
def __init__(self, config):
super().__init__()
self.has_layer_norm = config.gate_layer_norm
if self.has_layer_norm:
self.layer_norm = T5LayerNorm(config.hidden_size)
self.feed_forward = nn.Linear(config.hidden_size, 1)
self._init_weights(self.feed_forward)
self.activation = ScaledSigmoid(config.sigmoid_mask_scale)
self.use_gumbel_noise = config.use_gumbel_noise
def forward(self, hidden_states, input_ids):
if self.has_layer_norm:
hidden_states = self.layer_norm(hidden_states)
delete_gate_logits = self.feed_forward(hidden_states)
# Add gumbel noise to the delete gate logits
if self.training and self.use_gumbel_noise:
gumbel_noise = gumbel_noise_like(delete_gate_logits)
delete_gate_logits += gumbel_noise
gate_values = self.activation(delete_gate_logits)
# Check if there are any pad tokens in input_ids
if (input_ids == 0).any():
# Set gate values for pad tokens (input_ids == 0) to sigmoid_mask_scale
pad_mask = (input_ids == 0).unsqueeze(-1)
gate_values = torch.where(pad_mask, torch.tensor(self.activation.sigmoid_mask_scale), gate_values)
return gate_values, delete_gate_logits
def _init_weights(self, m, init_func="xavier_uniform_"):
# Initialize the weights. This is necessary because
# HuggingFace disables initialization during "from_pretrained"
if isinstance(m, nn.Linear):
TORCH_INIT_FUNCTIONS[init_func](m.weight)
m.bias.data.fill_(1)
class LogSigmoidDeleteGate(SigmoidDeleteGate):
def __init__(self, config):
super().__init__(config)
self.activation = nn.LogSigmoid()
class RandomDeleteGate(nn.Module):
def __init__(self, config):
super().__init__()
# Store the sigmoid_mask_scale and the probability of activation
self.sigmoid_mask_scale = config.sigmoid_mask_scale
self.random_deletion_probability = config.random_deletion_probability
def __random_mask_tensor(self, x, n):
# Determine the shape for the output tensor
target_shape = (x.shape[0], x.shape[1], 1)
total_elements = x.shape[0] * x.shape[1]
# Create a flattened float tensor of all 0.0
flat_tensor = torch.zeros(total_elements, dtype=torch.float32, device=x.device)
# Randomly select n indices to be set to 1.0
indices = torch.randperm(total_elements)[:n]
flat_tensor[indices] = 1.0
# Reshape it to match the desired target shape
float_tensor = flat_tensor.view(target_shape)
return float_tensor
def forward(self, hidden_states, input_ids):
# Calculate the number of tokens to delete using a gaussian
deletion_percentage = np.random.normal(loc=self.random_deletion_probability, scale=0.05)
n_deletions = int(deletion_percentage * hidden_states.shape[0] * hidden_states.shape[1])
# Create a random mask with n_deletions True values
random_mask = self.__random_mask_tensor(hidden_states, n_deletions)
# Scale the mask by sigmoid_mask_scale
delete_gate_mask = random_mask * self.sigmoid_mask_scale
return delete_gate_mask, delete_gate_mask
class FixedDeleteGate(nn.Module):
def __init__(self, config):
super().__init__()
self.sigmoid_mask_scale = config.sigmoid_mask_scale
self.fixed_deletion_amount = config.fixed_deletion_amount
self.sep_tokens = torch.tensor([12, 13, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
46, 47, 48, 49, 50, 61, 62, 63, 64, 65, 66, 67, 94,
95, 96, 97, 98, 99, 126, 127, 128, 129, 1])
def __create_mask(self, input_ids):
device = input_ids.device
batch_size, seq_len = input_ids.size()
self.sep_tokens = self.sep_tokens.to(device)
# Create an initial mask filled with sigmoid_mask_scale
mask = torch.full((batch_size, seq_len), self.sigmoid_mask_scale, device=device)
# Find sep_token indices
is_sep = torch.isin(input_ids, self.sep_tokens)
# Create a tensor of segment lengths
sep_positions = torch.cumsum(is_sep, dim=1)
segment_lengths = torch.zeros_like(input_ids, dtype=torch.float)
segment_lengths[:, 1:] = (sep_positions[:, 1:] != sep_positions[:, :-1]).float()
segment_lengths[:, 0] = 1.0
segment_lengths = torch.cumsum(segment_lengths, dim=1)
# Calculate number of zeros for each segment
segment_counts = torch.bincount(sep_positions.view(-1), minlength=seq_len)
segment_starts = torch.cumsum(torch.cat([torch.tensor([0], device=device), segment_counts[:-1]]), dim=0)
segment_ends = torch.cumsum(segment_counts, dim=0)
num_zeros = torch.ceil((1 - self.fixed_deletion_amount) * (segment_ends - segment_starts)).long()
# Create the mask based on the calculated number of zeros
for i in range(batch_size):
for start, count in zip(segment_starts, num_zeros):
mask[i, start:start + count] = 0
return mask.to(torch.float)
def forward(self, hidden_states, input_ids):
delete_gate_mask = self.__create_mask(input_ids).unsqueeze(-1)
return delete_gate_mask, delete_gate_mask
class MrT5Attention(T5Attention):
"""
Extends the T5Attention class to include a delete gate. Only the forward
method is modified. The delete_gate_mask passed to the forward function
is applied to the attention scores.
"""
def __init__(self, config: MrT5Config, has_relative_attention_bias=False):
super().__init__(config, has_relative_attention_bias)
#### NEW CODE ####
self.use_softmax1 = config.use_softmax1
#### NEW CODE ####
def forward(
self,
hidden_states,
mask=None,
key_value_states=None,
position_bias=None,
past_key_value=None,
layer_head_mask=None,
query_length=None,
use_cache=False,
output_attentions=False,
#### NEW CODE ####
delete_gate_mask=None,
#### NEW CODE ####
):
"""
Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
"""
# Input is (batch_size, seq_length, dim)
# Mask is (batch_size, key_length) (non-causal) or (batch_size, key_length, key_length)
# past_key_value[0] is (batch_size, n_heads, q_len - 1, dim_per_head)
batch_size, seq_length = hidden_states.shape[:2]
real_seq_length = seq_length
if past_key_value is not None:
if len(past_key_value) != 2:
raise ValueError(
f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
)
real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length
key_length = real_seq_length if key_value_states is None else key_value_states.shape[
1]
def shape(states):
"""projection"""
return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
def unshape(states):
"""reshape"""
return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
def project(hidden_states, proj_layer, key_value_states, past_key_value):
"""projects hidden states correctly to key/query states"""
if key_value_states is None:
# self-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(hidden_states))
elif past_key_value is None:
# cross-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(key_value_states))
if past_key_value is not None:
if key_value_states is None:
# self-attn
# (batch_size, n_heads, key_length, dim_per_head)
hidden_states = torch.cat(
[past_key_value, hidden_states], dim=2)
elif past_key_value.shape[2] != key_value_states.shape[1]:
# checking that the `sequence_length` of the `past_key_value` is the same as
# the provided `key_value_states` to support prefix tuning
# cross-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(proj_layer(key_value_states))
else:
# cross-attn
hidden_states = past_key_value
return hidden_states
# get query states
# (batch_size, n_heads, seq_length, dim_per_head)
query_states = shape(self.q(hidden_states))
# get key/value states
key_states = project(
hidden_states, self.k, key_value_states, past_key_value[
0] if past_key_value is not None else None
)
value_states = project(
hidden_states, self.v, key_value_states, past_key_value[
1] if past_key_value is not None else None
)
# compute scores
scores = torch.matmul(
query_states, key_states.transpose(3, 2)
) # equivalent of torch.einsum("bnqd,bnkd->bnqk", query_states, key_states), compatible with onnx op>9
#### NEW CODE ####
if not self.has_absolute_position_embeddings:
#### NEW CODE ####
if position_bias is None:
if not self.has_relative_attention_bias:
position_bias = torch.zeros(
(1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype
)
if self.gradient_checkpointing and self.training:
position_bias.requires_grad = True
else:
position_bias = self.compute_bias(
real_seq_length, key_length, device=scores.device)
# if key and values are already calculated
# we want only the last query position bias
if past_key_value is not None:
position_bias = position_bias[:, :, -hidden_states.size(1):, :]
if mask is not None:
# (batch_size, n_heads, seq_length, key_length)
position_bias = position_bias + mask
if self.pruned_heads:
mask = torch.ones(position_bias.shape[1])
mask[list(self.pruned_heads)] = 0
position_bias_masked = position_bias[:, mask.bool()]
else:
position_bias_masked = position_bias
scores = scores + position_bias_masked
#### NEW CODE ####
# If there is no position bias, add attention mask to scores directly
elif mask is not None:
scores = scores + mask
#### NEW CODE ####
# Log scores to return for loss calculation
scores_to_return = scores
#### NEW CODE ####
# Apply the mask from the delete gate
if delete_gate_mask is not None:
scores = scores + delete_gate_mask.squeeze(-1).unsqueeze(-2).unsqueeze(-2)
if self.use_softmax1:
attn_weights = softmax1(scores.float(), dim=-1).type_as(
scores)
else:
attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(
scores
) # (batch_size, n_heads, seq_length, key_length)
#### NEW CODE ####
attn_weights = nn.functional.dropout(
attn_weights, p=self.dropout, training=self.training
) # (batch_size, n_heads, seq_length, key_length)
# Mask heads if we want to
if layer_head_mask is not None:
attn_weights = attn_weights * layer_head_mask
# (batch_size, seq_length, dim)
attn_output = unshape(torch.matmul(attn_weights, value_states))
attn_output = self.o(attn_output)
present_key_value_state = (key_states, value_states) if (
self.is_decoder and use_cache) else None
outputs = (attn_output,) + \
(present_key_value_state,) + (position_bias,)
if output_attentions:
attentions_keys_queries = (attn_weights, key_states, query_states, value_states, scores_to_return)
outputs = outputs + (attentions_keys_queries,)
return outputs
class MrT5LayerSelfAttention(nn.Module):
"""
Modified version of T5LayerSelfAttention that uses MrT5Attention instead
of T5Attention.
"""
def __init__(self, config, has_relative_attention_bias=False):
super().__init__()
#### NEW CODE ####
# Use MrT5Attention instead of T5Attention
self.SelfAttention = MrT5Attention(
config, has_relative_attention_bias=has_relative_attention_bias)
#### NEW CODE ####
self.layer_norm = T5LayerNorm(
config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(
self,
hidden_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
#### NEW CODE ####
delete_gate_mask=None,
#### NEW CODE ####
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.SelfAttention(
normed_hidden_states,
mask=attention_mask,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
#### NEW CODE ####
delete_gate_mask=delete_gate_mask,
#### NEW CODE ####
)
hidden_states = hidden_states + self.dropout(attention_output[0])
# add attentions if we output them
outputs = (hidden_states,) + attention_output[1:]
return outputs
class MrT5LayerCrossAttention(nn.Module):
"""
Modified version of T5LayerCrossAttention that uses MrT5Attention instead
of T5Attention.
"""
def __init__(self, config):
super().__init__()
#### NEW CODE ####
# Use MrT5Attention instead of T5Attention
self.EncDecAttention = MrT5Attention(
config, has_relative_attention_bias=False)
#### NEW CODE ####
self.layer_norm = T5LayerNorm(
config.d_model, eps=config.layer_norm_epsilon)
self.dropout = nn.Dropout(config.dropout_rate)
def forward(
self,
hidden_states,
key_value_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
query_length=None,
output_attentions=False,
#### NEW CODE ####
delete_gate_mask=None,
#### NEW CODE ####
):
normed_hidden_states = self.layer_norm(hidden_states)
attention_output = self.EncDecAttention(
normed_hidden_states,
mask=attention_mask,
key_value_states=key_value_states,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
query_length=query_length,
output_attentions=output_attentions,
#### NEW CODE ####
delete_gate_mask=delete_gate_mask,
#### NEW CODE ####
)
layer_output = hidden_states + self.dropout(attention_output[0])
# add attentions if we output them
outputs = (layer_output,) + attention_output[1:]
return outputs
class MrT5Block(nn.Module):
"""
Modified version of T5Block that uses MrT5LayerSelfAttention and
MrT5LayerCrossAttention instead of T5LayerSelfAttention and
T5LayerCrossAttention.
"""
def __init__(self, config, has_relative_attention_bias=False,
#### NEW CODE ####
has_delete_gate=False,
#### NEW CODE ####
):
super().__init__()
self.is_decoder = config.is_decoder
self.layer = nn.ModuleList()
#### NEW CODE ####
# Use MrT5LayerSelfAttention and MrT5LayerCrossAttention
# instead of T5LayerSelfAttention and T5LayerCrossAttention
self.layer.append(MrT5LayerSelfAttention(
config, has_relative_attention_bias=has_relative_attention_bias))
if self.is_decoder:
self.layer.append(MrT5LayerCrossAttention(config))
#### NEW CODE ####
self.layer.append(T5LayerFF(config))
#### NEW CODE ####
# Add delete gate if needed
self.has_delete_gate = has_delete_gate
if self.has_delete_gate:
if config.deletion_type == "scaled_sigmoid":
self.delete_gate = SigmoidDeleteGate(config)
elif config.deletion_type == "log_sigmoid":
self.delete_gate = LogSigmoidDeleteGate(config)
elif config.deletion_type == "random":
self.delete_gate = RandomDeleteGate(config)
elif config.deletion_type == "fixed":
self.delete_gate = FixedDeleteGate(config)
else:
raise ValueError(
f"Invalid deletion type: {config.deletion_type}")
# Set hard_delete flags
self.sigmoid_mask_scale = config.sigmoid_mask_scale
self.deletion_threshold = config.deletion_threshold
#### NEW CODE ####
#### NEW CODE ####
def __get_new_positions_and_mask(self, batch_size, seq_len, delete_gate_mask, deletion_threshold, device):
delete_gate_mask = delete_gate_mask.squeeze(-1)
# Create filter from delete gate mask
deletion_threshold = deletion_threshold if deletion_threshold is not None else self.deletion_threshold
keep_this = delete_gate_mask > deletion_threshold
# Calculate the target position for each token
target_pos = torch.cumsum(keep_this, dim=1) - 1
new_len = target_pos[:, -1].max().item() + 1
# Clamp the target position to avoid out of bounds when deleting everything
target_pos = target_pos.clamp(min=0)
# Map the positions to the src side. Do this in int32, because it's faster and we will not have sequences
# longer than 2^31
positions = torch.arange(seq_len, device=device, dtype=torch.int32).repeat(batch_size, 1)
positions *= keep_this.int()
src_side_pos = torch.zeros(batch_size, new_len, device=device, dtype=torch.int32)
src_side_pos.scatter_add_(1, target_pos, positions)
# Create the new mask
new_mask = torch.arange(new_len, device=device).expand(batch_size, -1) <= target_pos[:, -1:]
new_mask = (~new_mask).float() * -1e9
new_mask = new_mask.unsqueeze(-1)
return src_side_pos.long(), new_mask
def __hard_delete_hidden_states(self, hidden_states, positions):
new_hidden_states = torch.gather(hidden_states, 1, positions.unsqueeze(2).expand(-1, -1, hidden_states.size(2)))
return new_hidden_states
def __hard_delete_4_dimensions(self, position_bias, positions):
new_position_bias = torch.gather(position_bias, 1, positions.unsqueeze(2).unsqueeze(3).expand(-1, -1, position_bias.size(2), position_bias.size(3)))
return new_position_bias
#### NEW CODE ####
def forward(
self,
hidden_states,
attention_mask=None,
position_bias=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
layer_head_mask=None,
cross_attn_layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
return_dict=True,
#### NEW CODE ####
delete_gate_mask=None,
input_ids=None,
hard_delete=None,
deletion_threshold=None,
#### NEW CODE ####
):
if past_key_value is not None:
if not self.is_decoder:
logger.warning(
"`past_key_values` is passed to the encoder. Please make sure this is intended.")
expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
if len(past_key_value) != expected_num_past_key_values:
raise ValueError(
f"There should be {expected_num_past_key_values} past states. "
f"{'2 (key / value) for cross attention. ' if expected_num_past_key_values == 4 else ''}"
f"Got {len(past_key_value)} past key / value states"
)
self_attn_past_key_value = past_key_value[:2]
cross_attn_past_key_value = past_key_value[2:]
else:
self_attn_past_key_value, cross_attn_past_key_value = None, None
##### NEW CODE #####
# Initialize delete gate values and logits for logging/loss calculation
delete_gate_values = None
delete_gate_logits = None
if self.has_delete_gate:
delete_gate_values, delete_gate_logits = self.delete_gate(
hidden_states, input_ids)
delete_gate_mask = delete_gate_values
# Raise error if all tokens are deleted in any sequence in batch
if (delete_gate_values < self.deletion_threshold).all():
raise ValueError("All tokens are deleted in this batch. " + \
"Please adjust the deletion rate or " + \
"alpha hyperparameter.")
# Apply hard deletion
if hard_delete:
# Compute new token positions
new_positions, delete_gate_mask = self.__get_new_positions_and_mask(
hidden_states.size(0), hidden_states.size(1), delete_gate_mask, deletion_threshold, hidden_states.device)
# Compute new position bias
if position_bias is not None:
new_position_bias = self.__hard_delete_4_dimensions(
position_bias.permute(0, 2, 3, 1), new_positions)
new_position_bias = self.__hard_delete_4_dimensions(
new_position_bias.permute(0, 2, 1, 3), new_positions)
position_bias = new_position_bias.permute(0, 3, 2, 1)
# Compute new attention mask
new_attention_mask = self.__hard_delete_4_dimensions(
attention_mask.permute(0, 3, 1, 2), new_positions)
attention_mask = new_attention_mask.permute(0, 2, 3, 1)
# Compute new hidden states and delete gate mask
hidden_states = self.__hard_delete_hidden_states(
hidden_states, new_positions)
##### NEW CODE #####
self_attention_outputs = self.layer[0](
hidden_states,
attention_mask=attention_mask,
position_bias=position_bias,
layer_head_mask=layer_head_mask,
past_key_value=self_attn_past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
#### NEW CODE ####
# Only apply delete_gate_mask to self-attention if the block
# is the encoder
delete_gate_mask=None if self.is_decoder else delete_gate_mask,
#### NEW CODE ####
)
hidden_states, present_key_value_state = self_attention_outputs[:2]
# Keep self-attention outputs and relative position weights
attention_outputs = self_attention_outputs[2:]
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(
hidden_states, min=-clamp_value, max=clamp_value)
do_cross_attention = self.is_decoder and encoder_hidden_states is not None
if do_cross_attention:
# the actual query length is unknown for cross attention
# if using past key value states. Need to inject it here
if present_key_value_state is not None:
query_length = present_key_value_state[0].shape[2]
else:
query_length = None
cross_attention_outputs = self.layer[1](
hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
position_bias=encoder_decoder_position_bias,
layer_head_mask=cross_attn_layer_head_mask,
past_key_value=cross_attn_past_key_value,
query_length=query_length,
use_cache=use_cache,
output_attentions=output_attentions,
#### NEW CODE ####
delete_gate_mask=delete_gate_mask,
#### NEW CODE ####
)
hidden_states = cross_attention_outputs[0]
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(
hidden_states, min=-clamp_value, max=clamp_value)
# Combine self attn and cross attn key value states
if present_key_value_state is not None:
present_key_value_state = present_key_value_state + \
cross_attention_outputs[1]
# Keep cross-attention outputs and relative position weights
attention_outputs = attention_outputs + cross_attention_outputs[2:]
# Apply Feed Forward layer
hidden_states = self.layer[-1](hidden_states)
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
hidden_states = torch.clamp(
hidden_states, min=-clamp_value, max=clamp_value)
outputs = (hidden_states,)
if use_cache:
outputs = outputs + (present_key_value_state,) + attention_outputs
else:
outputs = outputs + attention_outputs
##### NEW CODE #####
if self.has_delete_gate:
outputs = outputs + \
(delete_gate_values, delete_gate_logits, delete_gate_mask, attention_mask)
##### NEW CODE #####
# hidden-states, present_key_value_states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights), (delete_gate_mask), (delete_gate_logits)
return outputs
class MrT5Stack(T5Stack):
def __init__(self, config, embed_tokens=None):
super().__init__(config, embed_tokens)
##### NEW CODE #####
if self.is_decoder:
self.block = nn.ModuleList(
[
MrT5Block(
config, has_relative_attention_bias=bool(i == 0))
for i in range(config.num_layers)
]
)
else:
blocks = []
for i in range(config.num_layers):
blocks.append(
MrT5Block(
config,
# Only the first layer has relative attention bias
has_relative_attention_bias=bool(i == 0),
# Add delete gate if specified
has_delete_gate=bool(i == config.delete_gate_layer),
)
)
self.block = nn.ModuleList(blocks)
##### NEW CODE #####
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
inputs_embeds=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
#### NEW CODE ####
delete_gate_mask=None,
delete_gate_output=None,
delete_gate_logits=None,
hard_delete=None,
deletion_threshold=None,
#### NEW CODE ####
):
# Model parallel
if self.model_parallel:
torch.cuda.set_device(self.first_device)
self.embed_tokens = self.embed_tokens.to(self.first_device)
use_cache = use_cache if use_cache is not None else self.config.use_cache
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if input_ids is not None and inputs_embeds is not None:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(
f"You cannot specify both {err_msg_prefix}input_ids and {err_msg_prefix}inputs_embeds at the same time"
)
elif input_ids is not None:
input_shape = input_ids.size()
input_ids = input_ids.view(-1, input_shape[-1])
elif inputs_embeds is not None:
input_shape = inputs_embeds.size()[:-1]
else:
err_msg_prefix = "decoder_" if self.is_decoder else ""
raise ValueError(
f"You have to specify either {err_msg_prefix}input_ids or {err_msg_prefix}inputs_embeds")
if inputs_embeds is None:
if self.embed_tokens is None:
raise ValueError(
"You have to initialize the model with valid token embeddings")
inputs_embeds = self.embed_tokens(input_ids)
#### NEW CODE ####
if self.absolute_pos_embed is not None:
position_ids = torch.arange(input_shape[-1], dtype=torch.long, device=inputs_embeds.device)
position_embeds = self.absolute_pos_embed(position_ids)
inputs_embeds = inputs_embeds + position_embeds
#### NEW CODE ####
batch_size, seq_length = input_shape
# required mask seq length can be calculated via length of past
mask_seq_length = past_key_values[0][0].shape[2] + \
seq_length if past_key_values is not None else seq_length
if use_cache is True:
if not self.is_decoder:
raise ValueError(
f"`use_cache` can only be set to `True` if {self} is used as a decoder")
# initialize past_key_values with `None` if past does not exist
if past_key_values is None:
past_key_values = [None] * len(self.block)
if attention_mask is None:
attention_mask = torch.ones(
batch_size, mask_seq_length, device=inputs_embeds.device)
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
extended_attention_mask = self.get_extended_attention_mask(
attention_mask, input_shape)
# If a 2D or 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.is_decoder and encoder_hidden_states is not None:
encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
encoder_hidden_shape = (
encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
encoder_attention_mask = torch.ones(
encoder_hidden_shape, device=inputs_embeds.device, dtype=torch.long
)
encoder_extended_attention_mask = self.invert_attention_mask(
encoder_attention_mask)
else:
encoder_extended_attention_mask = None
if self.gradient_checkpointing and self.training:
if use_cache:
logger.warning_once(
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
)
use_cache = False
#### NEW CODE ####
# Return a new encoder attention mask if hard delete is enabled
attention_mask_to_return = None
#### NEW CODE ####
# Prepare head mask if needed
head_mask = self.get_head_mask(head_mask, self.config.num_layers)
cross_attn_head_mask = self.get_head_mask(
cross_attn_head_mask, self.config.num_layers)
present_key_value_states = () if use_cache else None
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
all_cross_attentions = () if (output_attentions and self.is_decoder) else None
position_bias = None
encoder_decoder_position_bias = None
#### NEW CODE ####
all_queries = () if output_attentions else None
all_keys = () if output_attentions else None
all_values = () if output_attentions else None
all_scores = () if output_attentions else None
all_cross_attn_queries = () if (output_attentions and self.is_decoder) else None
all_cross_attn_keys = () if (output_attentions and self.is_decoder) else None
all_cross_attn_values = () if (output_attentions and self.is_decoder) else None
all_cross_attn_scores = () if (output_attentions and self.is_decoder) else None
#### NEW CODE ####
hidden_states = self.dropout(inputs_embeds)
for i, (layer_module, past_key_value) in enumerate(zip(self.block, past_key_values)):
layer_head_mask = head_mask[i]
cross_attn_layer_head_mask = cross_attn_head_mask[i]
# Model parallel
if self.model_parallel:
torch.cuda.set_device(hidden_states.device)
# Ensure that attention_mask is always on the same device as hidden_states
if attention_mask is not None:
attention_mask = attention_mask.to(hidden_states.device)
if position_bias is not None:
position_bias = position_bias.to(hidden_states.device)
if encoder_hidden_states is not None:
encoder_hidden_states = encoder_hidden_states.to(
hidden_states.device)
if encoder_extended_attention_mask is not None:
encoder_extended_attention_mask = encoder_extended_attention_mask.to(
hidden_states.device)
if encoder_decoder_position_bias is not None:
encoder_decoder_position_bias = encoder_decoder_position_bias.to(
hidden_states.device)
if layer_head_mask is not None:
layer_head_mask = layer_head_mask.to(hidden_states.device)
if cross_attn_layer_head_mask is not None:
cross_attn_layer_head_mask = cross_attn_layer_head_mask.to(
hidden_states.device)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.forward,
hidden_states,
extended_attention_mask,
position_bias,
encoder_hidden_states,
encoder_extended_attention_mask,
encoder_decoder_position_bias,
layer_head_mask,
cross_attn_layer_head_mask,
None, # past_key_value is always None with gradient checkpointing
use_cache,
output_attentions,
#### NEW CODE ####
delete_gate_mask,
#### NEW CODE ####
)
else:
layer_outputs = layer_module(
hidden_states,
attention_mask=extended_attention_mask,
position_bias=position_bias,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
encoder_decoder_position_bias=encoder_decoder_position_bias,
layer_head_mask=layer_head_mask,
cross_attn_layer_head_mask=cross_attn_layer_head_mask,
past_key_value=past_key_value,
use_cache=use_cache,
output_attentions=output_attentions,
#### NEW CODE ####
delete_gate_mask=delete_gate_mask,
input_ids=input_ids,
hard_delete=hard_delete,
deletion_threshold=deletion_threshold,
#### NEW CODE ####
)
#### NEW CODE ####
# Update delete_gate_mask if the previous layer had a delete gate
if layer_module.has_delete_gate:
delete_gate_output, delete_gate_logits, delete_gate_mask, new_attention_mask = layer_outputs[-4], layer_outputs[-3], layer_outputs[-2], layer_outputs[-1]
# Update resized masks if the previous layer did a hard deletion
if hard_delete:
extended_attention_mask = new_attention_mask
attention_mask_to_return = extended_attention_mask.squeeze(-2).squeeze(-2)
attention_mask_to_return = (attention_mask_to_return == 0).int()
#### NEW CODE ####
# layer_outputs is a tuple with:
# hidden-states, key-value-states, (self-attention position bias), (self-attention weights), (cross-attention position bias), (cross-attention weights)
if use_cache is False:
layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
hidden_states, present_key_value_state = layer_outputs[:2]
# We share the position biases between the layers - the first layer store them
# layer_outputs = hidden-states, key-value-states (self-attention position bias), (self-attention weights),
# (cross-attention position bias), (cross-attention weights)
position_bias = layer_outputs[2]
if self.is_decoder and encoder_hidden_states is not None:
#### NEW CODE ####
index = 4 if output_attentions else 3
encoder_decoder_position_bias = layer_outputs[index]
#### NEW CODE ####
# append next layer key value states
if use_cache:
present_key_value_states = present_key_value_states + \
(present_key_value_state,)
#### NEW CODE ####
if output_attentions:
attn_weights, keys, queries, values, scores = layer_outputs[3]
all_attentions = all_attentions + (attn_weights,)
all_queries = all_queries + (queries,)
all_keys = all_keys + (keys,)
all_values = all_values + (values,)
all_scores = all_scores + (scores,)
if self.is_decoder:
cross_attn_weights, cross_attn_keys, cross_attn_queries, \
cross_attn_values, cross_attn_scores = layer_outputs[5]
all_cross_attentions = all_cross_attentions + \
(cross_attn_weights,)
all_cross_attn_queries = all_cross_attn_queries + \
(cross_attn_queries,)
all_cross_attn_keys = all_cross_attn_keys + \
(cross_attn_keys,)
all_cross_attn_values = all_cross_attn_values + \
(cross_attn_values,)
all_cross_attn_scores = all_cross_attn_scores + \
(cross_attn_scores,)
#### NEW CODE ####
# Model Parallel: If it's the last layer for that device, put things on the next device
if self.model_parallel:
for k, v in self.device_map.items():
if i == v[-1] and "cuda:" + str(k) != self.last_device:
hidden_states = hidden_states.to("cuda:" + str(k + 1))
hidden_states = self.final_layer_norm(hidden_states)
hidden_states = self.dropout(hidden_states)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(
v
for v in [
hidden_states,
present_key_value_states,
all_hidden_states,
all_attentions,
all_cross_attentions,
#### NEW CODE ####
delete_gate_mask,
delete_gate_output,
delete_gate_logits,
attention_mask_to_return,
all_queries,
all_keys,
all_values,
all_scores,
all_cross_attn_queries,
all_cross_attn_keys,
all_cross_attn_values,
all_cross_attn_scores,
#### NEW CODE ####
]
if v is not None
)
return MrT5BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
past_key_values=present_key_value_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
cross_attentions=all_cross_attentions,
#### NEW CODE ####
delete_gate_mask=delete_gate_mask,
delete_gate_output=delete_gate_output,
delete_gate_logits=delete_gate_logits,
attention_mask=attention_mask_to_return,
attention_queries=all_queries,
attention_keys=all_keys,
attention_values=all_values,
attention_scores=all_scores,
cross_attention_queries=all_cross_attn_queries,
cross_attention_keys=all_cross_attn_keys,
cross_attention_values=all_cross_attn_values,
cross_attention_scores=all_cross_attn_scores,
#### NEW CODE ####
)
class MrT5ForConditionalGeneration(T5ForConditionalGeneration):
config_class = MrT5Config
def __init__(self, config: MrT5Config):
super().__init__(config)
#### NEW CODE ####
encoder_config = copy.deepcopy(config)
encoder_config.is_decoder = False
encoder_config.use_cache = False
encoder_config.is_encoder_decoder = False
self.encoder = MrT5Stack(encoder_config, self.shared)
decoder_config = copy.deepcopy(config)
decoder_config.is_decoder = True
decoder_config.is_encoder_decoder = False
decoder_config.num_layers = config.num_decoder_layers
self.decoder = MrT5Stack(decoder_config, self.shared)
#### NEW CODE ####
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
decoder_head_mask: Optional[torch.FloatTensor] = None,
cross_attn_head_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
#### NEW CODE ####
hard_delete: bool = False,
deletion_threshold: Optional[float] = None,
#### NEW CODE ####
) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
# FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
if head_mask is not None and decoder_head_mask is None:
if self.config.num_layers == self.config.num_decoder_layers:
decoder_head_mask = head_mask
# Encode if needed (training, first prediction pass)
if encoder_outputs is None:
# Convert encoder inputs in embeddings if needed
encoder_outputs = self.encoder(
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
#### NEW CODE ####
hard_delete=hard_delete,
deletion_threshold=deletion_threshold,
#### NEW CODE ####
)
elif return_dict and not isinstance(encoder_outputs, BaseModelOutput):
#### NEW CODE ####
encoder_outputs = MrT5BaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=encoder_outputs.last_hidden_state,
hidden_states=encoder_outputs.hidden_states if 'hidden_states' in encoder_outputs else None,
attentions=encoder_outputs.attentions if 'attentions' in encoder_outputs else None,
delete_gate_mask=encoder_outputs.delete_gate_mask if 'delete_gate_mask' in encoder_outputs else None,
)
#### NEW CODE ####
#### NEW CODE ####
hidden_states = encoder_outputs.last_hidden_state
attention_mask = encoder_outputs.attention_mask if 'attention_mask' in encoder_outputs else attention_mask
#### NEW CODE ####
if self.model_parallel:
torch.cuda.set_device(self.decoder.first_device)
if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
# get decoder inputs from shifting lm labels to the right
decoder_input_ids = self._shift_right(labels)
# Set device for model parallelism
if self.model_parallel:
torch.cuda.set_device(self.decoder.first_device)
hidden_states = hidden_states.to(self.decoder.first_device)
if decoder_input_ids is not None:
decoder_input_ids = decoder_input_ids.to(
self.decoder.first_device)
if attention_mask is not None:
attention_mask = attention_mask.to(self.decoder.first_device)
if decoder_attention_mask is not None:
decoder_attention_mask = decoder_attention_mask.to(
self.decoder.first_device)
# Decode
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
inputs_embeds=decoder_inputs_embeds,
past_key_values=past_key_values,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
cross_attn_head_mask=cross_attn_head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
#### NEW CODE ####
delete_gate_mask=encoder_outputs.delete_gate_mask,
#### NEW CODE ####
)
sequence_output = decoder_outputs[0]
# Set device for model parallelism
if self.model_parallel:
torch.cuda.set_device(self.encoder.first_device)
self.lm_head = self.lm_head.to(self.encoder.first_device)
sequence_output = sequence_output.to(self.lm_head.weight.device)
if self.config.tie_word_embeddings:
# Rescale output before projecting on vocab
# See https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
sequence_output = sequence_output * (self.model_dim**-0.5)
lm_logits = self.lm_head(sequence_output)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss(ignore_index=-100)
# move labels to correct device to enable PP
labels = labels.to(lm_logits.device)
loss = loss_fct(
lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
# TODO(thom): Add z_loss https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666
if not return_dict:
output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
return ((loss,) + output) if loss is not None else output
##### NEW CODE #####
return MrT5Seq2SeqLMOutput(
loss=loss,
logits=lm_logits,
past_key_values=decoder_outputs.past_key_values,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
delete_gate_mask=encoder_outputs.delete_gate_mask,
delete_gate_output=encoder_outputs.delete_gate_output,
delete_gate_logits=encoder_outputs.delete_gate_logits,
encoder_keys=encoder_outputs.attention_keys,
encoder_queries=encoder_outputs.attention_queries,
encoder_values=encoder_outputs.attention_values,
encoder_scores=encoder_outputs.attention_scores,
decoder_keys=decoder_outputs.attention_keys,
decoder_queries=decoder_outputs.attention_queries,
decoder_values=decoder_outputs.attention_values,
decoder_scores=decoder_outputs.attention_scores,
cross_attention_queries=decoder_outputs.cross_attention_queries,
cross_attention_keys=decoder_outputs.cross_attention_keys,
cross_attention_values=decoder_outputs.cross_attention_values,
cross_attention_scores=decoder_outputs.cross_attention_scores,
)
##### NEW CODE #####
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
attention_mask=None,
head_mask=None,
decoder_head_mask=None,
decoder_attention_mask=None,
cross_attn_head_mask=None,
use_cache=None,
encoder_outputs=None,
**kwargs,
):
# cut decoder_input_ids if past_key_values is used
if past_key_values is not None:
past_length = past_key_values[0][0].shape[2]
# Some generation methods already pass only the last input ID
if input_ids.shape[1] > past_length:
remove_prefix_length = past_length
else:
# Default to old behavior: keep only final ID
remove_prefix_length = input_ids.shape[1] - 1
input_ids = input_ids[:, remove_prefix_length:]
##### NEW CODE #####
# TODO: Generation will need special handling of attention masks, which
# will need to be resized if hard delete is enabled. For now, we will
# simply omit the encoder attention mask for generation.
attention_mask = None
##### NEW CODE #####
return {
"decoder_input_ids": input_ids,
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"decoder_attention_mask": decoder_attention_mask,
"cross_attn_head_mask": cross_attn_head_mask,
"use_cache": use_cache,
}
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