Feature Extraction
Transformers
Safetensors
diva
custom_code
File size: 9,431 Bytes
547936a
 
 
 
 
 
 
 
 
 
 
 
 
cd5984d
547936a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
cd5984d
 
547936a
 
 
cd5984d
547936a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fcf1cf7
547936a
 
 
b5e9bbc
 
 
 
 
 
547936a
 
 
 
 
 
 
2a901db
547936a
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
import copy
import json
import os
from typing import Optional, Union

import gradio as gr
import librosa
import numpy as np
import torch
import torch.nn.functional as F
from datasets import Audio
from safetensors.torch import load, load_model
from torch import nn
from .configuring_diva import DiVAConfig
from transformers import (
    AutoProcessor,
    AutoTokenizer,
    LlamaForCausalLM,
    PreTrainedModel,
    WhisperForConditionalGeneration,
)


class WhisperConnector(nn.Module):
    def __init__(
        self,
    ):
        super().__init__()
        self.decoder = None
        self.projection = nn.Linear(1280, 4096)
        self.query_tokens = nn.Parameter(torch.randn(448, 1280))

    def forward(self, x, output_device="cuda:1"):
        bsz = x.shape[0]
        query_tokens = self.query_tokens[None, :, :].expand(bsz, -1, -1)
        virt_whisper_tokens = self.decoder(
            inputs_embeds=query_tokens, encoder_hidden_states=x
        )
        if self.projection.weight.shape[-1] == 5120:
            virtual_tokens = self.projection(virt_whisper_tokens[0].reshape(112, 5120))
        else:
            virtual_tokens = self.projection(virt_whisper_tokens[0])
        return virtual_tokens.to(output_device)


class DiVAModel(PreTrainedModel):
    config_class = DiVAConfig

    def __init__(
        self, via_path=None, config_dict={}, device_map=None, speech_encoder_device=None
    ):
        super().__init__(DiVAConfig.from_dict(config_dict))
        if speech_encoder_device is None:
            speech_encoder_device = "cuda:0"
        whisper = WhisperForConditionalGeneration.from_pretrained(
            "openai/whisper-large-v3"
        )
        connector = WhisperConnector()
        connector.decoder = copy.deepcopy(whisper.model.decoder)
        if via_path is not None:
            with open(via_path, "rb") as f:
                sd = load(f.read())

            with torch.no_grad():
                connector.query_tokens = nn.Parameter(sd["query_tokens"])
                connector.projection.weight = nn.Parameter(sd["projection.weight"].T)
                connector.projection.bias = nn.Parameter(sd["projection.bias"])
                wsd = {
                    key.replace("connector.", ""): sd[key]
                    for key in sd
                    if key.startswith("connector.")
                }
                connector.decoder.load_state_dict(wsd)

        if device_map == None:
            num_layers = 32
            num_gpus = 2
            device_map = dict(
                **{"model.embed_tokens": 1, "model.norm": 1, "lm_head": 2},
                **{
                    "model.layers." + str(i): 1 + (i // (num_layers // num_gpus))
                    for i in range(num_layers)
                },
            )

        self.connector = connector.to(speech_encoder_device)
        self.whisper_encoder = whisper.model.encoder.to(speech_encoder_device)
        self.llama_decoder = LlamaForCausalLM.from_pretrained(
            "meta-llama/Meta-Llama-3-8B-Instruct",
            device_map=device_map,
            torch_dtype=torch.float16,
        )
        self.processor = AutoProcessor.from_pretrained("openai/whisper-large-v3")
        self.tokenizer = AutoTokenizer.from_pretrained("WillHeld/via-llama")
        self.prefix = torch.tensor([128000, 128006, 882, 128007, 271]).to(
            self.llama_decoder.model.embed_tokens.weight.device
        )

        self.pre_user_suffix = torch.tensor(
            self.tokenizer.encode(
                "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n"
            )
        ).to(self.llama_decoder.model.embed_tokens.weight.device)
        self.final_header = torch.tensor([128009, 128006, 78191, 128007, 271]).to(
            self.llama_decoder.model.embed_tokens.weight.device
        )
        self.speech_encoder_device = speech_encoder_device

    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
        *model_args,
        config=None,
        cache_dir=None,
        **kwargs,
    ):
        if os.path.isdir(pretrained_model_name_or_path):
            via_path = (
                pretrained_model_name_or_path + "/model-00001-of-00004.safetensors"
            )
            config_path = pretrained_model_name_or_path + "/config.json"
        else:
            # Loading from huggingface repo
            from huggingface_hub import hf_hub_download

            hf_hub_download(
                repo_id=pretrained_model_name_or_path,
                filename="model-00001-of-00004.safetensors",
                token=kwargs.get("token", None),
                local_dir=os.path.dirname(__file__),
            )
            hf_hub_download(
                repo_id=pretrained_model_name_or_path,
                filename="config.json",
                token=kwargs.get("token", None),
                local_dir=os.path.dirname(__file__),
            )
            via_path = os.path.dirname(__file__) + "/model-00001-of-00004.safetensors"
            config_path = os.path.dirname(__file__) + "/config.json"
        with open(config_path, "r") as f:
            config_dict = json.loads(f.read())
        return cls(
            via_path,
            config_dict,
            kwargs["device_map"] if "device_map" in kwargs else "auto",
            (
                kwargs["speech_encoder_device"]
                if "speech_encoder_device" in kwargs
                else None
            ),
        )

    def forward(self, audio, prefix_text_tokens, suffix_text_tokens):
        inputs = self.processor(audio, return_tensors="pt", sampling_rate=16_000)
        input_features = inputs.input_features.to(self.speech_encoder_device)
        hidden_states = self.whisper_encoder(input_features=input_features)[
            "last_hidden_state"
        ]
        virt_tokens = self.connector(
            hidden_states,
            output_device=self.llama_decoder.model.embed_tokens.weight.device,
        ).squeeze()

        prefix_embed = self.llama_decoder.model.embed_tokens(prefix_text_tokens)
        suffix_embed = self.llama_decoder.model.embed_tokens(suffix_text_tokens)
        inputs_embeds = torch.cat(
            [prefix_embed, virt_tokens, suffix_embed], axis=0
        ).unsqueeze(0)

        outputs = self.llama_decoder(
            inputs_embeds=inputs_embeds.to(
                self.llama_decoder.model.embed_tokens.weight.device
            ).half(),
            return_dict=True,
            output_hidden_states=True,
            past_key_values=past_key_values,
        )

        return outputs

    def generate(
        self, audio, prompt, do_sample=False, logits_processor=None, max_new_tokens=128
    ):
        inputs = self.processor(audio, return_tensors="pt", sampling_rate=16_000)
        input_features = inputs.input_features.to(self.speech_encoder_device)
        hidden_states = self.whisper_encoder(input_features=input_features)[
            "last_hidden_state"
        ]
        virt_tokens = self.connector(
            hidden_states,
            output_device=self.llama_decoder.model.embed_tokens.weight.device,
        ).squeeze()

        if prompt != None and prompt != "":
            user_prompt_text = torch.tensor(
                self.tokenizer(prompt, add_special_tokens=False)["input_ids"],
                device=self.pre_user_suffix.device,
            )
            prefix = torch.cat(
                [self.pre_user_suffix, user_prompt_text, self.prefix], axis=0
            )
        else:
            prefix = self.prefix
        prefix_embed = self.llama_decoder.model.embed_tokens(prefix)
        suffix = self.final_header
        suffix_embed = self.llama_decoder.model.embed_tokens(suffix)
        inputs_embeds = torch.cat(
            [prefix_embed, virt_tokens, suffix_embed], axis=0
        ).unsqueeze(0)
        outs = []
        outputs = None
        greedy = 1
        i = 0
        while greedy != 128009 and len(outs) < max_new_tokens:
            past_key_values = outputs.past_key_values if outputs else None
            outputs = self.llama_decoder(
                inputs_embeds=inputs_embeds.to(
                    self.llama_decoder.model.embed_tokens.weight.device
                ).half(),
                return_dict=True,
                output_hidden_states=True,
                past_key_values=past_key_values,
            )
            next_token_logits = outputs.logits[-1, -1, :]

            if logits_processor:
                local_outs = torch.tensor(outs) if outs != [] else suffix
                local_outs = local_outs.reshape(1, -1)
                next_token_logits = logits_processor(
                    local_outs,
                    next_token_logits.reshape(1, -1),
                )
                next_token_logits = next_token_logits.flatten()
            if do_sample:
                logits = next_token_logits / temperature
                probs = F.softmax(logits, dim=-1)
                greedy = torch.multinomial(probs, num_samples=1)[0]
            else:
                greedy = next_token_logits.argmax()
            outs.append(greedy)
            next_embed = self.llama_decoder.model.embed_tokens(greedy.reshape(1, 1))
            inputs_embeds = next_embed
        return self.tokenizer.decode(outs, skip_special_tokens=True).replace(
            "<|eot_id|>", ""
        )