File size: 9,078 Bytes
ac916e4 68df9df ac916e4 68df9df ac916e4 68df9df ac916e4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 |
from collections import Counter, defaultdict
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor
from torch import nn
from torch.nn import CrossEntropyLoss
import copy
import math
from transformers.activations import gelu
from typing import List, Optional, Tuple, Union
from transformers.modeling_utils import PreTrainedModel, PretrainedConfig
from transformers import CONFIG_MAPPING
from transformers.modeling_outputs import BaseModelOutput
from transformers import GenerationConfig
from transformers import CLIPConfig, CLIPProcessor, CLIPModel, AutoModel
from transformers import WhisperConfig, WhisperPreTrainedModel, WhisperModel
from transformers import AutoConfig, AutoModelForCausalLM, LlamaConfig
def most_frequent_element(tensor):
flattened_list = tensor.flatten().tolist()
counter = Counter(flattened_list)
most_common_element = counter.most_common(1)[0][1]
return most_common_element
class MM_LLMs_Config(PretrainedConfig):
model_type = 'mm_llms'
is_composition = True
def __init__(
self,
audio_config=None,
llm_config=None,
audio_select_layer=-2,
**kwargs
):
self.audio_config = audio_config
self.llm_config = llm_config
self.audio_select_layer = audio_select_layer
if isinstance(self.audio_config, dict):
audio_config["model_type"] = (
audio_config["model_type"] if "model_type" in audio_config else "whisper"
)
self.audio_config = CONFIG_MAPPING[audio_config["model_type"]](**audio_config)
if isinstance(self.llm_config, dict):
llm_config["model_type"] = llm_config["model_type"] if "model_type" in llm_config else "llama"
self.llm_config = CONFIG_MAPPING[llm_config["model_type"]](**llm_config)
super().__init__(**kwargs)
class LlavaMultiModalProjector(nn.Module):
def __init__(self, in_hidden_size, out_hidden_size, conv_kernel=None, conv_stride=3):
super().__init__()
self.conv_kernel = conv_kernel
if conv_kernel:
self.linear_1 = nn.Conv1d(
in_hidden_size,
out_hidden_size,
kernel_size=conv_kernel,
stride=conv_stride)
else:
self.linear_1 = nn.Linear(
in_hidden_size,
out_hidden_size,
bias=True,
)
self.act = gelu
self.linear_2 = nn.Linear(
out_hidden_size,
out_hidden_size,
bias=True)
def forward(self, image_features):
hidden_states = self.linear_1(image_features)
if self.conv_kernel:
hidden_states = hidden_states.transpose(1, 2).contiguous()
hidden_states = self.act(hidden_states)
hidden_states = self.linear_2(hidden_states)
return hidden_states
class MM_LLMs(PreTrainedModel):
config_class = MM_LLMs_Config
supports_gradient_checkpointing = True
_supports_flash_attn_2 = True
def __init__(self, config, flash_attention=False, dtype=torch.float32):
super().__init__(config)
self.config = config
self.audio_encoder = AutoModel.from_config(config.audio_config)
self.llm = AutoModelForCausalLM.from_config(
config.llm_config,
use_flash_attention_2=flash_attention,
torch_dtype=dtype,
)
self.audio_projector = LlavaMultiModalProjector(
config.audio_config.d_model,
config.llm_config.hidden_size,
conv_kernel=40,
conv_stride=3,
)
def forward(self,
input_ids: torch.LongTensor = None,
image_index: torch.LongTensor = None,
audio_index: torch.LongTensor = None,
image_starts: torch.int = None,
image_ends: torch.int = None,
audio_starts: torch.int = None,
audio_ends: torch.int = None,
images: torch.FloatTensor = None,
audios: torch.FloatTensor = None,
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,
labels: Optional[torch.LongTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
use_cache: Optional[bool] = None,
return_dict: Optional[bool] = None, **kwargs):
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
audios = audios.type(self.audio_encoder.dtype) if audios is not None else None
model_inputs = self.prepare_inputs_for_generation(
input_ids=input_ids,
image_index=image_index,
audio_index=audio_index,
image_starts=image_starts,
image_ends=image_ends,
audio_starts=audio_starts,
audio_ends=audio_ends,
images=images,
audios=audios,
attention_mask=attention_mask,
labels=labels)
print(input_ids.shape, model_inputs['inputs_embeds'].shape)
outputs = self.llm(
inputs_embeds=model_inputs['inputs_embeds'],
attention_mask=model_inputs['attention_mask'],
labels=model_inputs['labels'],
return_dict=return_dict)
return outputs
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
inputs_embeds=None,
images=None,
audios=None,
audio_starts=None,
audio_ends=None,
image_starts=None,
image_ends=None,
attention_mask=None,
labels=None,
audio_index=None,
image_index=None,
**kwargs):
audio_features = self.encode_audio(
audios) if audios is not None else None
embed_tokens = self.llm.model.embed_tokens
text_embeddings = embed_tokens(input_ids)
batch_size = text_embeddings.shape[0]
seq_len = text_embeddings.shape[1]
embed_dim = text_embeddings.shape[2]
if len(audio_index):
max_count_audio = most_frequent_element(audio_index)
else:
max_count_audio = 0
if audio_features is not None:
seq_audio = audio_features.shape[1]
else:
seq_audio = 0
audio_len = seq_audio * max_count_audio
new_len = text_embeddings.shape[1] + audio_len
final_embedding = torch.zeros(
batch_size, new_len, embed_dim,
device=text_embeddings.device,
dtype=text_embeddings.dtype
)
final_embedding[:, :seq_len] = text_embeddings
final_attention_mask = torch.zeros(
batch_size, new_len,
device=attention_mask.device,
dtype=attention_mask.dtype
)
final_attention_mask[:, :seq_len] = attention_mask
if labels is not None:
final_labels = torch.full(
(batch_size, new_len),
-100,
device=labels.device,
dtype=labels.dtype
)
final_labels[:, :seq_len] = labels
else:
final_labels = None
audio_id = int(audio_starts[0])
where_is = torch.where(input_ids == audio_id)
positions = defaultdict(int)
b_audio = 0
for i in range(len(where_is[0])):
b, k = where_is[0][i], where_is[1][i]
int_b = int(b)
int_k = int(k)
f = audio_features[b_audio]
b_audio += 1
c = torch.cat([final_embedding[b, :int_k + 1 + positions[int_b]],
f, text_embeddings[b, k + 1:]])
final_embedding[b, :len(c)] = c
final_attention_mask[b, :len(c)] = 1.0
if labels is not None:
ignore = torch.tensor([-100] * len(f), device=labels.device)
c_label = torch.cat(
[final_labels[b, :int_k + 1 + positions[int_b]], ignore, labels[b, k + 1:]])
final_labels[b, :len(c)] = c_label
positions[int_b] += len(f)
model_inputs = {
"input_ids": input_ids,
"inputs_embeds": final_embedding,
"use_cache": kwargs.get("use_cache"),
"attention_mask": final_attention_mask,
"labels": final_labels,
}
return model_inputs
def encode_audio(self, audios):
encoded = self.audio_encoder.encoder(audios, output_hidden_states=True)
encoded = encoded.hidden_states[self.config.audio_select_layer]
audio_features = self.audio_projector(encoded.transpose(1, 2).contiguous())
return audio_features
|