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from transformers import PreTrainedModel, PretrainedConfig
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from model.architectures.transformer import EncoderDecoderTransformer
from model.architectures.crossformer import EncoderDecoderCrossFormer
from model.hf_configs import Seq2SeqConfig, Seq2SeqCrossConfig
from einops import rearrange
from tqdm import tqdm
class Seq2SeqTransformer(PreTrainedModel):
"""
Custom Transformer for Sequence to Sequence tasks.
"""
config_class = Seq2SeqConfig
base_model_prefix = "transformer"
def __init__(self, config: PretrainedConfig, device: Optional[str]=None):
super().__init__(config)
self.config = config
self.softmax = nn.Softmax(dim=-1)
self.transformer = EncoderDecoderTransformer(
src_vocab_size=config.vocab_size_src,
tgt_vocab_size=config.vocab_size_tgt,
embed_dim=config.d_model,
num_heads=config.n_heads,
ff_dim=config.d_ff,
num_encoder_layers=config.n_layers,
num_decoder_layers=config.n_layers,
max_seq_length=config.sequence_length
)
def _init_weights(self, module):
"""From Karpathy's Reproducing GPT2 video."""
if isinstance(module, nn.Linear):
std = 0.02
if hasattr(module, "SCALE_INIT"):
std *= (12)**-.5 # ~6 skips per encoder/decoder layer
torch.nn.init.normal_(module.weight, mean=0.0, std=std)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def _create_padding_mask(self, ids: torch.LongTensor) -> torch.DoubleTensor:
"""Creates a mask to avoid padded tokens to be interfering with attention"""
# First create boolean mask where True = padding token
is_padding = ids.eq(self.config.pad_token_id)
# Convert to float and replace padding positions with -inf, others with 1.0
mask = is_padding.float()
mask = mask.masked_fill(is_padding, float('-inf'))
mask = mask.masked_fill(~is_padding, 1.0)
return mask
def _shift_right(self, x: torch.LongTensor) -> torch.LongTensor:
"""Helper method to prepare decoder inputs (teacher forcing) by shifting right label tokens"""
shifted = torch.full(
(*x.shape[:-1], 1),
self.config.bos_token_id,
dtype=x.dtype,
device=x.device
)
shifted = torch.cat([shifted, x[:, :-1]], dim=-1)
return shifted
def _add_beginning_of_stream(self, x: torch.LongTensor) -> torch.LongTensor:
"""
Helper method to add BOS token to the beginning of input sequences
"""
bos = torch.full(
(*x.shape[:-1], 1),
self.config.bos_token_id,
dtype=x.dtype,
device=x.device
)
return torch.cat([bos, x], dim=-1)
def _add_end_of_stream(self, x: torch.LongTensor) -> torch.LongTensor:
"""Helper method to add EOS token to the end of label sequences"""
eos = torch.full(
(*x.shape[:-1], 1),
self.config.eos_token_id,
dtype=x.dtype,
device=x.device
)
return torch.cat([x, eos], dim=-1)
def forward(
self,
input_ids: torch.LongTensor,
labels: Optional[torch.LongTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
**kwargs
) -> Union[Tuple, dict]:
# TODO: add/end of streaming and right shift should take place outside of the model in tokenizer
# adding beginning of stream tokens to input too
input_ids = self._add_beginning_of_stream(input_ids)
# adding end of stream tokens to labels
labels = self._add_end_of_stream(labels)
# Prepare input for the decoder
if decoder_input_ids is None and labels is not None:
decoder_input_ids = self._shift_right(labels)
src_key_padding_mask = self._create_padding_mask(input_ids)
tgt_key_padding_mask = self._create_padding_mask(decoder_input_ids)
# Forward pass through your model
outputs = self.transformer(
src=input_ids,
tgt=decoder_input_ids,
src_mask=attention_mask,
tgt_mask=decoder_attention_mask,
src_key_padding_mask=src_key_padding_mask,
tgt_key_padding_mask=tgt_key_padding_mask
)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
loss = loss_fct(outputs.view(-1, self.config.vocab_size_tgt), labels.view(-1))
return dict(
loss=loss,
logits=outputs,
)
def generate(
self,
input_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor] = None,
max_length: Optional[int] = None,
temperature: float = 1.0,
do_sample: bool = False,
**kwargs
) -> torch.LongTensor:
batch_size = input_ids.shape[0]
max_length = max_length or self.config.max_length or 128
decoder_input_ids = torch.full(
(batch_size, 1),
self.config.bos_token_id,
dtype=torch.long,
device=input_ids.device
)
for _ in range(max_length - 1):
outputs = self.forward(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
)
next_token_logits = outputs["logits"][:, -1, :]
if do_sample:
# Apply temperature scaling
scaled_logits = next_token_logits / temperature
# Convert to probabilities
next_token_probs = self.softmax(scaled_logits)
# Sample from the probability distribution
next_token = torch.multinomial(
next_token_probs, num_samples=1
).squeeze(-1)
else:
# Greedy decoding
next_token = next_token_logits.argmax(dim=-1)
decoder_input_ids = torch.cat(
[decoder_input_ids, next_token.unsqueeze(-1)],
dim=-1
)
# Stop if all sequences have generated EOS token
if (decoder_input_ids == self.config.eos_token_id).any(dim=-1).all():
break
return decoder_input_ids
class Seq2SeqCrossFormer(Seq2SeqTransformer):
"""CrossFormer wrapper predicting over a discrete vocabulatory."""
config_class = Seq2SeqCrossConfig
def __init__(self, config: PretrainedConfig):
super().__init__(config)
self.softmax = nn.Softmax(dim=-1)
self.transformer = EncoderDecoderCrossFormer(
source_sequence_dimension=config.source_sequence_dimension,
target_sequence_dimension=config.target_sequence_dimension,
router_dim=config.router_dim,
src_vocab_size=config.vocab_size_src,
tgt_vocab_size=config.vocab_size_tgt,
embed_dim=config.d_model,
num_heads=config.n_heads,
ff_dim=config.d_ff,
num_encoder_layers=config.n_layers,
num_decoder_layers=config.n_layers,
max_seq_length=config.sequence_length
)
def _shift_right(self, x: torch.LongTensor) -> torch.LongTensor:
"""
Helper method to prepare decoder inputs (teacher forcing) by shifting right label tokens.
Handles 3D (B, S, C) tensors
"""
# Create shape that matches x's dimensions except for seq_len which will be 1
shape = list(x.shape)
shape[-2] = 1 # Set sequence dimension to 1
shifted = torch.full(
shape,
self.config.bos_token_id,
dtype=x.dtype,
device=x.device
)
shifted = torch.cat([shifted, x[..., :-1, :]], dim=-2)
return shifted
def _add_beginning_of_stream(self, x: torch.LongTensor) -> torch.LongTensor:
"""
Helper method to add BOS token to the beginning of input sequences.
Handles 3D (B, S, C) tensors
"""
shape = list(x.shape)
shape[-2] = 1 # Set sequence dimension to 1
sos = torch.full(
shape,
self.config.bos_token_id,
dtype=x.dtype,
device=x.device
)
return torch.cat([sos, x], dim=-2)
def _add_end_of_stream(self, x: torch.LongTensor) -> torch.LongTensor:
"""
Helper method to add EOS token to the end of label sequences.
Handles 3D (B, S, C) tensors
"""
# Create shape that matches x's dimensions except for seq_len which will be 1
shape = list(x.shape)
shape[-2] = 1 # Set sequence dimension to 1
eos = torch.full(
shape,
self.config.eos_token_id,
dtype=x.dtype,
device=x.device
)
return torch.cat([x, eos], dim=-2)
def forward(
self,
input_ids: torch.LongTensor,
labels: Optional[torch.LongTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
**kwargs
):
# FIXME: add/end of streaming and right shift should take place outside of the model in tokenizer
# (in tokenizer) adding beginning of stream tokens to input too
input_ids = self._add_beginning_of_stream(input_ids)
# (in tokenizer) adding end of stream tokens to labels
if labels is not None:
labels = self._add_end_of_stream(labels)
# Prepare input for the decoder
if decoder_input_ids is None and labels is not None:
decoder_input_ids = self._shift_right(labels)
src_src_key_padding_time_mask = rearrange(
self._create_padding_mask(
input_ids
),
'b s c -> (b c) s'
)
tgt_tgt_key_padding_time_mask = rearrange(
self._create_padding_mask(
decoder_input_ids
),
'b s c -> (b c) s'
)
# Forward pass through your model
outputs = self.transformer(
src=input_ids,
tgt=decoder_input_ids,
src_src_time_mask=kwargs.get("src_src_time_mask"),
src_src_dimension_mask=kwargs.get("src_src_dimension_mask"),
src_src_key_padding_time_mask=src_src_key_padding_time_mask,
tgt_tgt_time_mask=kwargs.get("tgt_tgt_time_mask"),
tgt_tgt_dimension_mask=kwargs.get("tgt_tgt_dimension_mask"),
tgt_tgt_key_padding_time_mask=tgt_tgt_key_padding_time_mask,
tgt_src_dimension_mask=kwargs.get("tgt_src_dimension_mask")
)
loss = None
if labels is not None:
loss_fct = nn.CrossEntropyLoss(
ignore_index=self.config.pad_token_id
)
loss = loss_fct(
outputs.view(-1, self.config.vocab_size_tgt), labels.view(-1)
)
return dict(
loss=loss,
logits=outputs,
)
def generate(
self,
input_ids: torch.LongTensor,
attention_mask: Optional[torch.Tensor]=None,
max_length: Optional[int]=None,
temperature: float=1.0,
do_sample: bool=False,
**kwargs
) -> torch.LongTensor:
batch_size, timesteps, channels = input_ids.shape
tgt_sequence_timesteps = timesteps+1 # src will gain a token at forward
max_length = max_length or self.config.max_length or 128
decoder_input_ids = torch.full(
(
batch_size,
tgt_sequence_timesteps,
self.config.target_sequence_dimension
),
self.config.pad_token_id,
dtype=torch.long,
device=input_ids.device
)
# Set BOS token at the start
decoder_input_ids[:, 0, :] = self.config.bos_token_id
generation_logits = []
for t in tqdm(range(timesteps+max_length), desc="Generating sequence"):
outputs = self.forward(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask
)
# Get predictions for this timestep
next_token_logits = outputs["logits"][:, t, :]
generation_logits.append(
next_token_logits.squeeze().detach().cpu().numpy()
)
if do_sample:
scaled_logits = next_token_logits / temperature
next_token_probs = self.softmax(scaled_logits)
next_token = torch.multinomial(
next_token_probs, num_samples=1
).squeeze(-1)
else:
next_token = next_token_logits.argmax(dim=-1)
try:
# Place the predicted token at position t+1
decoder_input_ids[:, t+1, :] = next_token
except IndexError:
break # last token for fixed-size element
# Check if all sequences have generated EOS token
if (next_token == self.config.eos_token_id).all():
break
# For forecasting, we only keep the last tgt_sequence_timesteps
# TODO: fix this
# decoder_input_ids = decoder_input_ids[
# :, -tgt_sequence_timesteps:, :
# ]
return decoder_input_ids, generation_logits
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