audio_modeling_ocean.py

import torch, random, fire
from transformers.models.whisper import WhisperConfig
from torch.nn import functional as F
from flash_attn import flash_attn_varlen_func
from torch import nn
import numpy as np
from transformers.activations import ACT2FN
import math

def sinusoids(length, channels, max_timescale=10000):
    """Returns sinusoids for positional embedding"""
    assert channels % 2 == 0
    log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
    inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2))
    scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
    return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1)

class OceanWhisperAttention(nn.Module):
    def __init__(self, embed_dim, num_heads):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.head_dim = embed_dim // num_heads

        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=True)
        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=True)
        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=True)

    def forward(self, hidden_states: torch.Tensor, seq_len: torch.Tensor):
        bsz, _ = hidden_states.size()

        query_states = self.q_proj(hidden_states).view(bsz, self.num_heads, self.head_dim)
        key_states = self.k_proj(hidden_states).view(bsz, self.num_heads, self.head_dim)
        value_states = self.v_proj(hidden_states).view(bsz, self.num_heads, self.head_dim)

        cu_len = F.pad(torch.cumsum(seq_len, dim=0), (1, 0), "constant", 0).to(torch.int32)
        max_seqlen = torch.max(seq_len).to(torch.int32).detach()
        attn_output = flash_attn_varlen_func(query_states, key_states, value_states, cu_len, cu_len, max_seqlen, max_seqlen, causal=False)  # (bsz * qlen, nheads, headdim)
        attn_output = attn_output.reshape(bsz, self.embed_dim)
        attn_output = self.out_proj(attn_output)
        return attn_output

class OceanWhisperEncoderLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.embed_dim = config.d_model
        self.self_attn = OceanWhisperAttention(self.embed_dim, config.encoder_attention_heads)
        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
        self.activation_fn = ACT2FN[config.activation_function]
        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
        self.final_layer_norm = nn.LayerNorm(self.embed_dim)

    def forward(self, hidden_states: torch.Tensor, seq_len: torch.Tensor) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states = self.self_attn(hidden_states, seq_len)
        hidden_states = residual + hidden_states
        residual = hidden_states
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.activation_fn(self.fc1(hidden_states))
        hidden_states = self.fc2(hidden_states)
        hidden_states = residual + hidden_states

        if (hidden_states.dtype == torch.float16 or hidden_states.dtype == torch.bfloat16) and (
            torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any()
        ):
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
        return hidden_states

class OceanAudioEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        config._attn_implementation = 'flash_attention_2'  #
        self.config = config
        self.max_source_positions = (config.max_audio_seconds * config.sampling_rate // config.hop_length) // config.stride_size
        self.embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0

        # 需要在LLM的初始化中注册注册
        self.conv1 = nn.Conv1d(config.num_mel_bins, config.d_model, kernel_size=config.kernel_size, padding=1)
        self.conv2 = nn.Conv1d(config.d_model, config.d_model, kernel_size=config.kernel_size, stride=config.stride_size, padding=1)
        self.register_buffer("positional_embedding", sinusoids(self.max_source_positions, config.d_model))  # 1500 * d

        self.layers = nn.ModuleList([OceanWhisperEncoderLayer(config) for _ in range(config.encoder_layers)])
        self.layer_norm = nn.LayerNorm(config.d_model)

        self.gradient_checkpointing = True

    @torch.no_grad()
    def fake_input(self, device):
        input_features = torch.rand([2, self.config.num_mel_bins, 10], dtype=torch.float32, device=device)
        encoder_length = torch.ones([2], dtype=torch.int32, device=device) * 3
        bridge_length = torch.ones([2], dtype=torch.int32, device=device)
        return input_features, encoder_length, bridge_length

    def forward(
        self,
        input_features,
        output_length,  # MAKESURE 输入的必须是两次conv计算后的hidden state长度
    ):
        input_features = input_features.to(self.conv1.weight.dtype)
        inputs_embeds = nn.functional.gelu(self.conv1(input_features))  # (bs, channels, frames)
        inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))  # (bs, channels, frames // 2)
        inputs_embeds = inputs_embeds.permute(0, 2, 1)  # (bs, frams, channels) 
        bsz, tgt_len, _ = inputs_embeds.size()  # 当前batch最大长度
        if tgt_len < self.positional_embedding.shape[0]:  
            current_positional_embedding = self.positional_embedding[:tgt_len]
        else:
            current_positional_embedding = self.positional_embedding
        hidden_states = (inputs_embeds.to(torch.float32) + current_positional_embedding).to(inputs_embeds.dtype)

        # packing hidden states
        attention_mask = torch.arange(0, tgt_len).to(hidden_states.device)
        attention_mask = torch.lt(attention_mask, output_length.reshape(bsz, 1)).view(bsz, tgt_len, 1)
        unpacking_index = torch.cumsum(attention_mask.to(torch.int32).view(-1), dim=0) - 1  # 转成下标
        hidden_states = torch.masked_select(hidden_states, attention_mask).view(torch.sum(output_length), self.config.d_model)

        for idx, encoder_layer in enumerate(self.layers):
            if self.gradient_checkpointing and self.training:
                def create_custom_forward(module):
                    def custom_forward(*inputs):
                        return module(*inputs)
                    return custom_forward
                hidden_states = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(encoder_layer),
                    hidden_states,
                    output_length
                )
            else:
                hidden_states = encoder_layer(hidden_states, output_length)
        hidden_states = self.layer_norm(hidden_states)
        # unpacking
        hidden_states = torch.index_select(hidden_states, 0, unpacking_index).view(bsz, tgt_len, self.config.d_model)
        hidden_states = torch.where(attention_mask, hidden_states, 0)
        return hidden_states

class OceanAudioBridge(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config.audio_config
        if self.config.avg_pooler > 1:
            self.avg_pooler = nn.AvgPool1d(self.config.avg_pooler, stride=2)
        else:
            self.avg_pooler = None
        self.proj1 = nn.Linear(self.config.d_model, config.hidden_size)
        self.proj2 = nn.Linear(config.hidden_size, config.hidden_size)

    def forward(self, x, output_length): 
        if self.avg_pooler is not None:
            x = x.permute(0, 2, 1)
            x = self.avg_pooler(x)
            x = x.permute(0, 2, 1)

        batch_size, sl, _ = x.shape
        output_length = output_length.to(x.device)
        valid_mask = torch.arange(0, sl).to(x.device) 
        valid_mask = torch.lt(valid_mask, output_length.reshape(batch_size, 1)).reshape(batch_size, sl, 1)
        x = torch.masked_select(x, valid_mask).reshape(-1, self.config.d_model) # (sum(valid_sequence_length), d)
        x = ACT2FN[self.config.activation_function](self.proj1(x))
        x = self.proj2(x)
        return x


def test_audio():
    from transformers import AutoConfig
    from processor_ocean import OceanAudioProcessor
    # from ..configuration_ocean import OceanConfig
    config = AutoConfig.from_pretrained("./", trust_remote_code=True)

    config.audio_config.d_model = 24
    config.audio_config.encoder_layers = 2
    config.audio_config.encoder_attention_heads = 4
    config.audio_config.encoder_ffn_dim = 48

    ae = OceanAudioEncoder(config.audio_config).cuda().to(torch.bfloat16)
    bg = OceanAudioBridge(config).cuda().to(torch.bfloat16)
    l = random.randint(10, 30)
    bs = 3
    input_length = torch.tensor([random.randint(1, l) for _ in range(bs)])
    encoder_length, bridge_length = OceanAudioProcessor.inference_output_length(config.audio_config, input_length)
    print("l={}, input_valid_length={},\nencoder_valid_length={}, bridge_valid_length={}".format(l, input_length, encoder_length, bridge_length))
    wave_features = torch.rand((bs, config.audio_config.num_mel_bins, l))
    a = ae(wave_features.to('cuda'), encoder_length.to('cuda'))
    b = bg(a, bridge_length.to('cuda'))
    print('encoder output={}, bridge output={}'.format(a.shape, b.shape))
    print(a)
    print(b)


if __name__ == '__main__':
    fire.Fire()