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# Copyright (c) 2024 Amphion.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from transformers import LlamaConfig, LlamaForCausalLM, LlamaModel
import torch
import torch.nn.functional as F
import numpy as np
import os
import torch.nn as nn
from typing import List, Optional, Tuple, Union
import math
from transformers.models.llama.modeling_llama import LlamaDecoderLayer
from transformers.models.llama.modeling_llama import BaseModelOutputWithPast
# sinusoidal positional encoding
class SinusoidalPosEmb(nn.Module):
def __init__(self, dim):
super().__init__()
self.dim = dim
def forward(self, x):
device = x.device
half_dim = self.dim // 2
emb = math.log(10000) / (half_dim - 1)
emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
emb = x[:, None] * emb[None, :] * 1.0
emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
return emb
class LlamaAdaptiveRMSNorm(nn.Module):
def __init__(self, hidden_size=1024, eps=1e-6, dim_cond=1024):
super().__init__()
self.to_weight = nn.Linear(dim_cond, hidden_size)
nn.init.zeros_(self.to_weight.weight)
nn.init.ones_(self.to_weight.bias)
self.variance_epsilon = eps
self._is_hf_initialized = True # disable automatic init
def forward(self, hidden_states, cond_embedding):
input_dtype = hidden_states.dtype
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
weight = self.to_weight(cond_embedding)
if len(weight.shape) == 2:
weight = weight.unsqueeze(1)
return (weight * hidden_states).to(input_dtype)
class LlamaNARDecoderLayer(LlamaDecoderLayer):
def __init__(self, config: LlamaConfig, layer_idx: int):
"""Override to adaptive layer norm"""
super().__init__(config, layer_idx) # init attention, mlp, etc.
self.input_layernorm = LlamaAdaptiveRMSNorm(
config.hidden_size, eps=config.rms_norm_eps, dim_cond=config.hidden_size
)
self.post_attention_layernorm = LlamaAdaptiveRMSNorm(
config.hidden_size, eps=config.rms_norm_eps, dim_cond=config.hidden_size
)
# add `cond` in forward function
def forward(
self,
hidden_states: torch.Tensor,
cond_embedding: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
hidden_states = self.input_layernorm(
hidden_states, cond_embedding=cond_embedding
)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(
hidden_states, cond_embedding=cond_embedding
)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
def __init__(self, config: LlamaConfig, layer_idx: int):
"""Override to adaptive layer norm"""
super().__init__(config, layer_idx) # init attention, mlp, etc.
self.layer_idx = layer_idx
self.input_layernorm = LlamaAdaptiveRMSNorm(
config.hidden_size, eps=config.rms_norm_eps, dim_cond=config.hidden_size
)
self.post_attention_layernorm = LlamaAdaptiveRMSNorm(
config.hidden_size, eps=config.rms_norm_eps, dim_cond=config.hidden_size
)
def forward(
self,
hidden_states: torch.Tensor,
cond_embedding: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
) -> Tuple[
torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
returned tensors for more detail.
use_cache (`bool`, *optional*):
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
(see `past_key_values`).
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
"""
residual = hidden_states
hidden_states = self.input_layernorm(
hidden_states, cond_embedding=cond_embedding
)
# Self Attention
hidden_states, self_attn_weights, present_key_value = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
)
hidden_states = residual + hidden_states
# Fully Connected
residual = hidden_states
hidden_states = self.post_attention_layernorm(
hidden_states, cond_embedding=cond_embedding
)
hidden_states = self.mlp(hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights,)
if use_cache:
outputs += (present_key_value,)
return outputs
class DiffLlama(LlamaModel):
def __init__(
self,
hidden_size=1024,
num_heads=16,
num_layers=16,
config=LlamaConfig(0, 256, 1024, 1, 1),
):
super().__init__(config)
self.layers = nn.ModuleList(
[
LlamaNARDecoderLayer(
LlamaConfig(
hidden_size=hidden_size,
num_attention_heads=num_heads,
max_position_embeddings=4096,
intermediate_size=hidden_size * 4,
),
layer_idx=i,
)
for i in range(num_layers)
]
)
self.norm = LlamaAdaptiveRMSNorm(hidden_size, dim_cond=hidden_size)
self.diff_step_embedding = SinusoidalPosEmb(hidden_size)
self.diff_step_mlp = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 4),
nn.SiLU(),
nn.Linear(hidden_size * 4, hidden_size),
)
# self.position_embedding = PositionalEncoding(hidden_size, dropout=0.0)
self.cond_mlp = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 4),
nn.SiLU(),
nn.Linear(hidden_size * 4, hidden_size),
)
for layer in self.layers:
layer.input_layernorm = LlamaAdaptiveRMSNorm(
hidden_size, dim_cond=hidden_size
)
layer.post_attention_layernorm = LlamaAdaptiveRMSNorm(
hidden_size, dim_cond=hidden_size
)
self.post_init()
# self.reset_parameters()
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
# create noncausal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
def _expand_mask(
mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None
):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = (
mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(dtype).min
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
).to(inputs_embeds.device)
combined_attention_mask = (
expanded_attn_mask
if combined_attention_mask is None
else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward(
self,
x,
diffusion_step,
cond,
x_mask,
input_ids: torch.LongTensor = None, # [num_quant, B, T]
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
# retrieve some shape info
batch_size, seq_length, _ = x.shape
# condtion mlp
cond_embedding = self.cond_mlp(cond) # (B, T, C)
# diffusion step embedding
diffusion_step = self.diff_step_embedding(diffusion_step).to(x.device)
diffusion_step = self.diff_step_mlp(diffusion_step) # (B, C)
x = x + cond_embedding
inputs_embeds = x
attention_mask = x_mask
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
)
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
)
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length,
seq_length + past_key_values_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past),
dtype=torch.bool,
device=inputs_embeds.device,
)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
)
hidden_states = inputs_embeds
if self.gradient_checkpointing and self.training:
if use_cache:
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = (
past_key_values[idx] if past_key_values is not None else None
)
if self.gradient_checkpointing and self.training:
raise NotImplementedError
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cond_embedding=diffusion_step,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states, cond_embedding=diffusion_step)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
return hidden_states
class DiffLlamaPrefix(LlamaModel):
def __init__(
self,
hidden_size=1024,
num_heads=16,
num_layers=16,
config=LlamaConfig(0, 256, 1024, 1, 1),
):
super().__init__(config)
self.layers = nn.ModuleList(
[
LlamaNARDecoderLayer(
LlamaConfig(
hidden_size=hidden_size,
num_attention_heads=num_heads,
max_position_embeddings=4096,
intermediate_size=hidden_size * 4,
),
layer_idx=i,
)
for i in range(num_layers)
]
)
self.norm = LlamaAdaptiveRMSNorm(hidden_size, dim_cond=hidden_size)
self.diff_step_embedding = SinusoidalPosEmb(hidden_size)
self.diff_step_mlp = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 4),
nn.SiLU(),
nn.Linear(hidden_size * 4, hidden_size),
)
self.cond_mlp = nn.Sequential(
nn.Linear(hidden_size, hidden_size * 4),
nn.SiLU(),
nn.Linear(hidden_size * 4, hidden_size),
)
for layer in self.layers:
layer.input_layernorm = LlamaAdaptiveRMSNorm(
hidden_size, dim_cond=hidden_size
)
layer.post_attention_layernorm = LlamaAdaptiveRMSNorm(
hidden_size, dim_cond=hidden_size
)
self.embed_tokens = None
self.post_init()
def _prepare_decoder_attention_mask(
self, attention_mask, input_shape, inputs_embeds, past_key_values_length
):
# create noncausal mask
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
combined_attention_mask = None
def _expand_mask(
mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None
):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
bsz, src_len = mask.size()
tgt_len = tgt_len if tgt_len is not None else src_len
expanded_mask = (
mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
)
inverted_mask = 1.0 - expanded_mask
return inverted_mask.masked_fill(
inverted_mask.to(torch.bool), torch.finfo(dtype).min
)
if attention_mask is not None:
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
expanded_attn_mask = _expand_mask(
attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]
).to(inputs_embeds.device)
combined_attention_mask = (
expanded_attn_mask
if combined_attention_mask is None
else expanded_attn_mask + combined_attention_mask
)
return combined_attention_mask
def forward(
self,
x,
diffusion_step,
x_mask,
phone_embedding: Optional[torch.LongTensor] = None,
phone_mask: Optional[torch.FloatTensor] = None,
input_ids: torch.LongTensor = None, # [num_quant, B, T]
attention_mask: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPast]:
# retrieve some shape info
phone_embedding = self.cond_mlp(phone_embedding) # (B, T, C)
phone_length = phone_embedding.shape[1]
inputs_embeds = torch.cat([phone_embedding, x], dim=1)
attention_mask = torch.cat([phone_mask, x_mask], dim=1)
# diffusion step embedding
diffusion_step = self.diff_step_embedding(diffusion_step).to(x.device)
diffusion_step = self.diff_step_mlp(diffusion_step) # (B, C)
batch_size, seq_length, _ = inputs_embeds.shape
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
)
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
)
seq_length_with_past = seq_length
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
seq_length_with_past = seq_length_with_past + past_key_values_length
if position_ids is None:
device = input_ids.device if input_ids is not None else inputs_embeds.device
position_ids = torch.arange(
past_key_values_length,
seq_length + past_key_values_length,
dtype=torch.long,
device=device,
)
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
else:
position_ids = position_ids.view(-1, seq_length).long()
# embed positions
if attention_mask is None:
attention_mask = torch.ones(
(batch_size, seq_length_with_past),
dtype=torch.bool,
device=inputs_embeds.device,
)
attention_mask = self._prepare_decoder_attention_mask(
attention_mask,
(batch_size, seq_length),
inputs_embeds,
past_key_values_length,
)
hidden_states = inputs_embeds
if self.gradient_checkpointing and self.training:
if use_cache:
use_cache = False
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
next_decoder_cache = () if use_cache else None
for idx, decoder_layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
past_key_value = (
past_key_values[idx] if past_key_values is not None else None
)
if self.gradient_checkpointing and self.training:
raise NotImplementedError
else:
layer_outputs = decoder_layer(
hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
output_attentions=output_attentions,
use_cache=use_cache,
cond_embedding=diffusion_step,
)
hidden_states = layer_outputs[0]
if use_cache:
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
if output_attentions:
all_self_attns += (layer_outputs[1],)
hidden_states = self.norm(hidden_states, cond_embedding=diffusion_step)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
next_cache = next_decoder_cache if use_cache else None
return hidden_states[
:,
phone_length:,
]
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