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from dataclasses import dataclass
from functools import partial
from typing import Optional, Tuple, Union
import torch
from torch import nn
from transformers import AutoConfig, AutoModelForCausalLM
from transformers.cache_utils import DynamicCache
from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
ModelOutput,
)
from transformers.processing_utils import Unpack
from transformers.utils import logging
from .backbone_custom_modeling_qwen3 import CustomQwen3ForCausalLM
try:
from torch.nn.attention.flex_attention import BlockMask
except ImportError:
BlockMask = None
logger = logging.get_logger(__name__)
@dataclass
class EncoderBaseModelOutputWithPast(ModelOutput):
"""Custom (encoder) model output.
Stores previous decoder and updated encoder cache and encoder last hidden state.
"""
past_key_values: Optional[Union[Tuple[Tuple[torch.FloatTensor]], DynamicCache]] = (
None
)
encoder_last_hidden_state: Optional[torch.FloatTensor] = None
encoder_past_key_values: Optional[
Union[Tuple[Tuple[torch.FloatTensor]], DynamicCache]
] = None
@dataclass
class DecoderCausalLMOutputWithPast(ModelOutput):
"""Custom (decoder) model output.
Stores previous encoder and updated decoder cache and decoder logits.
"""
logits: Optional[torch.FloatTensor] = None
past_key_values: Optional[Union[Tuple[Tuple[torch.FloatTensor]], DynamicCache]] = (
None
)
encoder_past_key_values: Optional[
Union[Tuple[Tuple[torch.FloatTensor]], DynamicCache]
] = None
class LLMasEncoderDecoder(nn.Module):
def __init__(
self,
pretrained_model_name_or_path: str,
max_length: int,
attn_backend: str = "sdpa",
freeze_encoder: bool = False,
reinit_encoder: bool = False,
reinit_decoder: bool = False,
tie_encoder_decoder_weights: bool = False,
use_encoder_causal_mask: bool = False,
num_encoder_layers: int = -1,
num_decoder_layers: int = -1,
keep_top_encoder_layers: bool = False,
keep_top_decoder_layers: bool = False,
use_gradient_checkpointing: bool = False,
**llm_init_kwargs,
):
assert not (tie_encoder_decoder_weights and reinit_decoder), (
"Cannot tie encoder-decoder weights and reinitialize decoder."
)
assert not (tie_encoder_decoder_weights and freeze_encoder), (
"Cannot freeze encoder weights when tying encoder-decoder weights."
)
super().__init__()
self.use_encoder_causal_mask = use_encoder_causal_mask
self.tie_encoder_decoder_weights = tie_encoder_decoder_weights
if reinit_encoder:
assert num_encoder_layers > 0
encoder_config = AutoConfig.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=True,
num_hidden_layers=num_encoder_layers,
attn_implementation=attn_backend,
**llm_init_kwargs,
)
self.encoder = CustomQwen3ForCausalLM(encoder_config)
else:
self.encoder = CustomQwen3ForCausalLM.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=True,
attn_implementation=attn_backend,
**llm_init_kwargs,
)
assert num_encoder_layers <= len(self.encoder.model.layers), (
f"Cannot keep {num_encoder_layers} layers. "
f"Pre-trained model only has {len(self.encoder.model.layers)} layers."
)
num_encoder_layers = (
len(self.encoder.model.layers)
if num_encoder_layers == -1
else num_encoder_layers
)
if keep_top_encoder_layers:
self.encoder.model.layers = self.encoder.model.layers[
-num_encoder_layers:
]
else:
self.encoder.model.layers = self.encoder.model.layers[
:num_encoder_layers
]
if freeze_encoder:
for name, param in self.encoder.named_parameters():
if "embed_tokens" not in name:
param.requires_grad = False
if use_gradient_checkpointing:
self.encoder.gradient_checkpointing_enable()
if tie_encoder_decoder_weights:
self.decoder = self.encoder
num_decoder_layers = (
len(self.decoder.model.layers)
if num_decoder_layers == -1
else num_decoder_layers
)
assert num_decoder_layers <= len(self.decoder.model.layers), (
f"Cannot keep {num_decoder_layers} layers. "
f"Pre-trained model only has {len(self.decoder.model.layers)} layers."
)
# Keep **top** layers when tying weights
self.decoder_layer_idxs = list(range(len(self.encoder.model.layers)))[
-num_decoder_layers:
]
else:
if reinit_decoder:
assert num_decoder_layers > 0
decoder_config = AutoConfig.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=True,
num_hidden_layers=num_decoder_layers,
attn_implementation=attn_backend,
**llm_init_kwargs,
)
self.decoder = CustomQwen3ForCausalLM(decoder_config)
else:
self.decoder = CustomQwen3ForCausalLM.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=True,
attn_implementation=attn_backend,
**llm_init_kwargs,
)
assert num_decoder_layers <= len(self.decoder.model.layers), (
f"Cannot keep {num_decoder_layers} layers. "
f"Pre-trained model only has {len(self.decoder.layers)} layers."
)
if keep_top_decoder_layers:
self.decoder.model.layers = self.decoder.model.layers[
-num_decoder_layers:
]
else:
self.decoder.model.layers = self.decoder.model.layers[
:num_decoder_layers
]
del self.decoder.model.embed_tokens
# if in the original LM, the lm_head is weight-tied to embedding,
# point decoder lm_head to encoder's (instead of initializing separately)
if (
self.encoder.lm_head.weight.data_ptr()
== self.encoder.model.embed_tokens.weight.data_ptr()
):
self.decoder.lm_head = self.encoder.lm_head
else:
del self.encoder.lm_head
if use_gradient_checkpointing:
self.decoder.gradient_checkpointing_enable()
self.max_length = max_length
def freeze_encoder(self):
for p in self.encoder.model.parameters():
p.requires_grad = False
def unfreeze_encoder(self):
for p in self.encoder.model.parameters():
p.requires_grad = True
# noinspection PyUnusedLocal
def forward(
self,
# Decoder inputs
input_ids: torch.LongTensor,
attention_mask: Optional[Union[torch.FloatTensor, BlockMask]] = None,
position_ids: Optional[torch.LongTensor] = None,
cache_position: Optional[torch.LongTensor] = None,
past_key_values: Optional[DynamicCache] = None,
encoder_last_hidden_state: Optional[torch.FloatTensor] = None,
# Encoder inputs
encoder_input_ids: Optional[torch.LongTensor] = None,
encoder_attention_mask: Optional[Union[torch.FloatTensor, BlockMask]] = None,
encoder_position_ids: Optional[torch.LongTensor] = None,
encoder_cache_position: Optional[torch.LongTensor] = None,
encoder_past_key_values: Optional[DynamicCache] = None,
# Additional args
fix_cache_length: bool = True, # Not used; compatibility with other backbones
return_updated_cache: bool = False,
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
) -> Union[DecoderCausalLMOutputWithPast, EncoderBaseModelOutputWithPast]:
# During training/eval encoder_last_hidden_state is None.
# During generation encoder_last_hidden_state can be not None.
new_seen_tokens = (
0
if encoder_last_hidden_state is None
else encoder_last_hidden_state.shape[1]
)
# Encode clean tokens
if encoder_input_ids is not None:
if self.use_encoder_causal_mask:
encoder_attention_mask = None # None --> enforces use of causal mask
if encoder_cache_position is None and encoder_position_ids is not None:
encoder_cache_position = encoder_position_ids[0]
encoder_output = self.encoder.model(
input_ids=encoder_input_ids,
attention_mask=encoder_attention_mask,
position_ids=encoder_position_ids,
use_cache=True,
past_key_values=encoder_past_key_values,
cache_position=encoder_cache_position,
)
if return_updated_cache:
# encoder_output.past_key_values now contains latest encoder input
return EncoderBaseModelOutputWithPast(
encoder_last_hidden_state=encoder_output.last_hidden_state,
encoder_past_key_values=encoder_output.past_key_values,
past_key_values=past_key_values,
)
encoder_last_hidden_state = encoder_output.last_hidden_state
# Run decoder with xattn to clean token hidden states
if encoder_last_hidden_state is None: # No new encoder tokens
q_start_idx = 0
decoder_hidden_states = self.encoder.model.embed_tokens(input_ids)
if cache_position is None:
if position_ids is not None:
cache_position = position_ids[0]
else:
past_seen_tokens = (
past_key_values.get_seq_length()
if past_key_values is not None
else 0
)
cache_position = torch.arange(
past_seen_tokens,
past_seen_tokens + decoder_hidden_states.shape[1],
device=decoder_hidden_states.device,
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
decoder_position_embeddings = self.decoder.model.rotary_emb(
decoder_hidden_states, position_ids
)
else:
q_start_idx = encoder_last_hidden_state.shape[1]
decoder_hidden_states = self.encoder.model.embed_tokens(input_ids)
decoder_hidden_states = torch.cat(
[
encoder_last_hidden_state,
decoder_hidden_states,
],
dim=1,
)
if cache_position is None:
if position_ids is not None:
cache_position = position_ids[0]
else:
past_seen_tokens = (
past_key_values.get_seq_length()
if past_key_values is not None
else 0
)
cache_position = torch.cat(
[
torch.arange( # clean token position ids
past_seen_tokens,
past_seen_tokens + encoder_last_hidden_state.shape[1],
device=decoder_hidden_states.device,
),
torch.arange( # noisy position ids
past_seen_tokens + new_seen_tokens,
past_seen_tokens + new_seen_tokens + input_ids.shape[1],
device=decoder_hidden_states.device,
),
],
dim=-1,
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
decoder_position_embeddings = self.decoder.model.rotary_emb(
decoder_hidden_states, position_ids
)
if hasattr(self.decoder.model, "_update_causal_mask"): # bc on transformers
# noinspection PyProtectedMember
attention_mask = self.decoder.model._update_causal_mask(
attention_mask=attention_mask,
input_tensor=decoder_hidden_states,
cache_position=cache_position,
past_key_values=past_key_values,
output_attentions=False,
)
for decoder_layer in self.decoder.model.layers:
layer_idx = decoder_layer.self_attn.layer_idx
if (
self.tie_encoder_decoder_weights
and layer_idx not in self.decoder_layer_idxs
):
continue
# past_key_values gets updated in-place.
# Record previous length to re-truncate after each layer forward
if past_key_values is not None and len(past_key_values) > layer_idx:
prev_cache_len = past_key_values[layer_idx][0].shape[-2] # type: ignore
else:
prev_cache_len = 0
cache_len = prev_cache_len + new_seen_tokens
if self.decoder.model.gradient_checkpointing and self.training:
# noinspection PyProtectedMember
decoder_hidden_states = self.decoder._gradient_checkpointing_func(
partial(decoder_layer.__call__, **flash_attn_kwargs),
decoder_hidden_states, # hidden_states=,
attention_mask, # attention_mask=,
position_ids, # position_ids=,
past_key_values, # past_key_value=,
False, # output_attentions=,
True, # use_cache=,
cache_position, # cache_position=,
decoder_position_embeddings, # position_embeddings=,
q_start_idx, # q_start_idx=
)[0] # Shape: (input_ids.shape[0], input_ids.shape[1], hidden_dim)
else:
decoder_hidden_states = decoder_layer(
hidden_states=decoder_hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=False,
use_cache=True,
cache_position=cache_position,
position_embeddings=decoder_position_embeddings,
q_start_idx=q_start_idx, # Indicates where to slice output
**flash_attn_kwargs,
)[0] # Shape: (input_ids.shape[0], input_ids.shape[1], hidden_dim)
# Update decoder_hidden_states
if q_start_idx > 0:
decoder_hidden_states = torch.cat(
[
encoder_last_hidden_state,
decoder_hidden_states,
],
dim=1,
)
if past_key_values is not None:
# DynamicCache extends along sequence dimension by default;
# truncate back to original cache len + encoder output length
past_key_values.key_cache[layer_idx] = past_key_values.key_cache[
layer_idx
][..., :cache_len, :]
past_key_values.value_cache[layer_idx] = past_key_values.value_cache[
layer_idx
][..., :cache_len, :]
decoder_hidden_states = self.decoder.model.norm(
decoder_hidden_states[:, q_start_idx:, :]
)
logits = self.decoder.lm_head(decoder_hidden_states)
return DecoderCausalLMOutputWithPast(
logits=logits,
past_key_values=past_key_values,
encoder_past_key_values=encoder_past_key_values,
# Do not need to store encoder_last_hidden_state.
# If it was passed in, then it has become part of the past_key_values cache.
)
class LLMasEncoderDecoderShareKV(nn.Module):
def __init__(
self,
pretrained_model_name_or_path: str,
max_length: int,
attn_backend: str = "sdpa",
freeze_encoder: bool = False,
reinit_encoder: bool = False,
reinit_decoder: bool = False,
tie_encoder_decoder_weights: bool = False,
use_encoder_causal_mask: bool = False,
num_encoder_layers: int = -1,
num_decoder_layers: int = -1,
keep_top_encoder_layers: bool = False,
keep_top_decoder_layers: bool = False,
use_gradient_checkpointing: bool = False,
**llm_init_kwargs,
):
assert not (tie_encoder_decoder_weights and reinit_decoder), (
"Cannot tie encoder-decoder weights and reinitialize decoder."
)
assert not (tie_encoder_decoder_weights and freeze_encoder), (
"Cannot freeze encoder weights when tying encoder-decoder weights."
)
super().__init__()
self.use_encoder_causal_mask = use_encoder_causal_mask
self.tie_encoder_decoder_weights = tie_encoder_decoder_weights
if reinit_encoder:
assert num_encoder_layers > 0
encoder_config = AutoConfig.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=True,
num_hidden_layers=num_encoder_layers,
attn_implementation=attn_backend,
**llm_init_kwargs,
)
self.encoder = AutoModelForCausalLM.from_config(encoder_config)
else:
self.encoder = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=True,
attn_implementation=attn_backend,
**llm_init_kwargs,
)
assert num_encoder_layers <= len(self.encoder.model.layers), (
f"Cannot keep {num_encoder_layers} layers. "
f"Pre-trained model only has {len(self.encoder.model.layers)} layers."
)
num_encoder_layers = (
len(self.encoder.model.layers)
if num_encoder_layers == -1
else num_encoder_layers
)
if keep_top_encoder_layers:
self.encoder.model.layers = self.encoder.model.layers[
-num_encoder_layers:
]
else:
self.encoder.model.layers = self.encoder.model.layers[
:num_encoder_layers
]
if freeze_encoder:
for name, param in self.encoder.named_parameters():
if "embed_tokens" not in name:
param.requires_grad = False
if use_gradient_checkpointing:
self.encoder.gradient_checkpointing_enable()
if tie_encoder_decoder_weights:
self.decoder = self.encoder
num_decoder_layers = (
len(self.decoder.model.layers)
if num_decoder_layers == -1
else num_decoder_layers
)
assert num_decoder_layers <= len(self.decoder.model.layers), (
f"Cannot keep {num_decoder_layers} layers. "
f"Pre-trained model only has {len(self.decoder.model.layers)} layers."
)
# Keep **top** layers when tying weights
self.decoder_layer_idxs = list(range(len(self.encoder.model.layers)))[
-num_decoder_layers:
]
else:
if reinit_decoder:
assert num_decoder_layers > 0
decoder_config = AutoConfig.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=True,
num_hidden_layers=num_decoder_layers,
attn_implementation=attn_backend,
**llm_init_kwargs,
)
self.decoder = AutoModelForCausalLM(decoder_config)
else:
self.decoder = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path,
trust_remote_code=True,
attn_implementation=attn_backend,
**llm_init_kwargs,
)
assert num_decoder_layers <= len(self.decoder.model.layers), (
f"Cannot keep {num_decoder_layers} layers. "
f"Pre-trained model only has {len(self.decoder.layers)} layers."
)
if keep_top_decoder_layers:
self.decoder.model.layers = self.decoder.model.layers[
-num_decoder_layers:
]
else:
self.decoder.model.layers = self.decoder.model.layers[
:num_decoder_layers
]
del self.decoder.model.embed_tokens
# Even for frozen encoder, ensure embedding tokens are trainable
self.encoder.model.embed_tokens.requires_grad_(True)
unused_self_attn_params = ["o_proj", "q_norm", "q_proj"]
unused_layernorm_params = ["input_layernorm", "post_attention_layernorm"]
for unused_param in unused_self_attn_params:
if hasattr(self.encoder.model.layers[-1].self_attn, unused_param):
getattr(
self.encoder.model.layers[-1].self_attn, unused_param
).requires_grad_(False)
self.encoder.model.layers[-1].mlp.requires_grad_(False)
self.encoder.model.norm.requires_grad_(False)
for unused_param in unused_layernorm_params:
if hasattr(self.encoder.model.layers[-1], unused_param):
getattr(self.encoder.model.layers[-1], unused_param).requires_grad_(
False
)
# if in the original LM, the lm_head is weight-tied to embedding,
# point decoder lm_head to encoder's (instead of initializing separately)
if (
self.encoder.lm_head.weight.data_ptr()
== self.encoder.model.embed_tokens.weight.data_ptr()
):
self.decoder.lm_head = self.encoder.lm_head
else:
del self.encoder.lm_head
if use_gradient_checkpointing:
self.decoder.gradient_checkpointing_enable()
self.max_length = max_length
def freeze_encoder(self):
for p in self.encoder.model.parameters():
p.requires_grad = False
def unfreeze_encoder(self):
for p in self.encoder.model.parameters():
p.requires_grad = True
# noinspection PyUnusedLocal
def forward(
self,
# Decoder inputs
input_ids: torch.LongTensor,
attention_mask: Optional[Union[torch.FloatTensor, BlockMask]] = None,
position_ids: Optional[torch.LongTensor] = None,
cache_position: Optional[torch.LongTensor] = None,
past_key_values: Optional[DynamicCache] = None,
encoder_last_hidden_state: Optional[torch.FloatTensor] = None, # Not used
# Encoder inputs
encoder_input_ids: Optional[torch.LongTensor] = None,
encoder_attention_mask: Optional[Union[torch.FloatTensor, BlockMask]] = None,
encoder_position_ids: Optional[torch.LongTensor] = None,
encoder_cache_position: Optional[torch.LongTensor] = None,
encoder_past_key_values: Optional[DynamicCache] = None, # Not used
# Additional args
fix_cache_length: bool = True, # Not used; compatibility with other backbones
return_updated_cache: bool = False,
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
) -> Union[CausalLMOutputWithPast, BaseModelOutputWithPast]:
# Encode clean tokens
if encoder_input_ids is not None:
if self.use_encoder_causal_mask:
encoder_attention_mask = None # None --> enforces use of causal mask
if encoder_cache_position is None and encoder_position_ids is not None:
encoder_cache_position = encoder_position_ids[0]
past_key_values = self.encoder.model(
input_ids=encoder_input_ids,
attention_mask=encoder_attention_mask,
position_ids=encoder_position_ids,
use_cache=True,
past_key_values=past_key_values,
cache_position=encoder_cache_position,
).past_key_values
if return_updated_cache:
# encoder_output.past_key_values now contains latest encoder input
return BaseModelOutputWithPast(
past_key_values=past_key_values,
)
# Run decoder with xattn to clean token hidden states
decoder_hidden_states = self.encoder.model.embed_tokens(input_ids)
if cache_position is None:
if position_ids is not None:
cache_position = position_ids[0]
else: # During training / validation position_ids are not provided
cache_position = torch.arange(
decoder_hidden_states.shape[1],
device=decoder_hidden_states.device,
)
if position_ids is None:
position_ids = cache_position.unsqueeze(0)
decoder_position_embeddings = self.decoder.model.rotary_emb(
decoder_hidden_states, position_ids
)
if hasattr(self.decoder.model, "_update_causal_mask"): # bc on transformers
# noinspection PyProtectedMember
attention_mask = self.decoder.model._update_causal_mask(
attention_mask=attention_mask,
input_tensor=decoder_hidden_states,
cache_position=cache_position,
past_key_values=past_key_values,
output_attentions=False,
)
for decoder_layer in self.decoder.model.layers:
layer_idx = decoder_layer.self_attn.layer_idx
if (
self.tie_encoder_decoder_weights
and layer_idx not in self.decoder_layer_idxs
):
continue
# past_key_values gets updated in-place.
# Record previous length to truncate after each layer forward
if past_key_values is not None and len(past_key_values) > layer_idx:
prev_cache_len = past_key_values[layer_idx][0].shape[-2] # type: ignore
else:
prev_cache_len = 0
decoder_hidden_states = decoder_layer(
hidden_states=decoder_hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_values,
output_attentions=False,
use_cache=True,
cache_position=position_ids[0],
position_embeddings=decoder_position_embeddings,
**flash_attn_kwargs,
)[0] # Shape: (input_ids.shape[0], input_ids.shape[1], hidden_dim)
if past_key_values is not None:
# DynamicCache extends along sequence dimension by default;
# truncate back to original cache len + encoder output length
past_key_values.key_cache[layer_idx] = past_key_values.key_cache[
layer_idx
][..., :prev_cache_len, :]
past_key_values.value_cache[layer_idx] = past_key_values.value_cache[
layer_idx
][..., :prev_cache_len, :]
decoder_hidden_states = self.decoder.model.norm(decoder_hidden_states)
logits = self.decoder.lm_head(decoder_hidden_states)
return CausalLMOutputWithPast(
logits=logits,
past_key_values=past_key_values,
)
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