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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel
from transformers.models.wav2vec2.modeling_wav2vec2 import (
Wav2Vec2Model,
Wav2Vec2PreTrainedModel,
)
from torch.nn.functional import silu
from torch.nn.functional import softplus
from einops import rearrange, einsum
from torch import Tensor
from einops import rearrange
# DEVICE = torch.device('cuda')
DEVICE = torch.device('cpu')
## Audio models
class CustomMambaBlock(nn.Module):
def __init__(self, d_input, d_model, dropout=0.1):
super().__init__()
self.in_proj = nn.Linear(d_input, d_model)
self.s_B = nn.Linear(d_model, d_model)
self.s_C = nn.Linear(d_model, d_model)
self.out_proj = nn.Linear(d_model, d_input)
self.norm = nn.LayerNorm(d_input)
self.dropout = nn.Dropout(dropout)
self.activation = nn.ReLU()
def forward(self, x):
x_in = x # сохраняем вход
x = self.in_proj(x)
B = self.s_B(x)
C = self.s_C(x)
x = x + B + C
x = self.activation(x)
x = self.out_proj(x)
x = self.dropout(x)
x = self.norm(x + x_in) # residual + norm
return x
class CustomMambaClassifier(nn.Module):
def __init__(self, input_size=1024, d_model=256, num_layers=2, num_classes=7, dropout=0.1):
super().__init__()
self.input_proj = nn.Linear(input_size, d_model)
self.blocks = nn.ModuleList([
CustomMambaBlock(d_model, d_model, dropout=dropout)
for _ in range(num_layers)
])
self.fc = nn.Linear(d_model, num_classes)
def forward(self, x, lengths, with_features=False):
# x: (batch, seq_length, input_size)
x = self.input_proj(x)
for block in self.blocks:
x = block(x)
pooled = []
for i, l in enumerate(lengths):
if l > 0:
pooled.append(x[i, :l, :].mean(dim=0))
else:
pooled.append(torch.zeros(x.size(2), device=x.device))
pooled = torch.stack(pooled, dim=0)
if with_features:
return self.fc(pooled), x
else:
return self.fc(pooled)
def get_model_mamba(params):
return CustomMambaClassifier(
input_size=params.get("input_size", 1024),
d_model=params.get("d_model", 256),
num_layers=params.get("num_layers", 2),
num_classes=params.get("num_classes", 7),
dropout=params.get("dropout", 0.1)
)
class EmotionModel(Wav2Vec2PreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.config = config
self.wav2vec2 = Wav2Vec2Model(config)
self.init_weights()
def forward(self, input_values):
outputs = self.wav2vec2(input_values)
hidden_states = outputs[0] # (batch_size, sequence_length, hidden_size)
return hidden_states
## Text models
class Embedding():
def __init__(self, model_name='jinaai/jina-embeddings-v3', pooling=None):
self.model_name = model_name
self.pooling = pooling
self.device = DEVICE
self.tokenizer = AutoTokenizer.from_pretrained(model_name, code_revision='da863dd04a4e5dce6814c6625adfba87b83838aa', trust_remote_code=True)
self.model = AutoModel.from_pretrained(model_name, code_revision='da863dd04a4e5dce6814c6625adfba87b83838aa', trust_remote_code=True).to(self.device)
self.model.eval()
def _mean_pooling(self, X):
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0]
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
encoded_input = self.tokenizer(X, padding=True, truncation=True, return_tensors='pt').to(self.device)
with torch.no_grad():
model_output = self.model(**encoded_input)
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)
return sentence_embeddings.unsqueeze(1)
def get_embeddings(self, X, max_len):
encoded_input = self.tokenizer(X, padding=True, truncation=True, return_tensors='pt').to(self.device)
with torch.no_grad():
features = self.model(**encoded_input)[0].detach().cpu().float().numpy()
res = np.pad(features[:, :max_len, :], ((0, 0), (0, max(0, max_len - features.shape[1])), (0, 0)), "constant")
return torch.tensor(res)
class RMSNorm(nn.Module):
def __init__(self, d_model: int, eps: float = 1e-8) -> None:
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(d_model))
def forward(self, x: Tensor) -> Tensor:
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim = True) + self.eps) * self.weight
class Mamba(nn.Module):
def __init__(self, num_layers, d_input, d_model, d_state=16, d_discr=None, ker_size=4, num_classes=7, max_tokens=95, model_name='jina', pooling=None):
super().__init__()
mamba_par = {
'd_input' : d_input,
'd_model' : d_model,
'd_state' : d_state,
'd_discr' : d_discr,
'ker_size': ker_size
}
self.model_name = model_name
self.max_tokens = max_tokens
embed = Embedding(model_name, pooling)
self.embedding = embed.get_embeddings
self.layers = nn.ModuleList([nn.ModuleList([MambaBlock(**mamba_par), RMSNorm(d_input)]) for _ in range(num_layers)])
self.fc_out = nn.Linear(d_input, num_classes)
self.device = DEVICE
def forward(self, seq, cache=None, with_features=True):
seq = self.embedding(seq, self.max_tokens).to(self.device)
for mamba, norm in self.layers:
out, cache = mamba(norm(seq), cache)
seq = out + seq
if with_features:
return self.fc_out(seq.mean(dim = 1)), seq
else:
return self.fc_out(seq.mean(dim = 1))
class MambaBlock(nn.Module):
def __init__(self, d_input, d_model, d_state=16, d_discr=None, ker_size=4):
super().__init__()
d_discr = d_discr if d_discr is not None else d_model // 16
self.in_proj = nn.Linear(d_input, 2 * d_model, bias=False)
self.out_proj = nn.Linear(d_model, d_input, bias=False)
self.s_B = nn.Linear(d_model, d_state, bias=False)
self.s_C = nn.Linear(d_model, d_state, bias=False)
self.s_D = nn.Sequential(nn.Linear(d_model, d_discr, bias=False), nn.Linear(d_discr, d_model, bias=False),)
self.conv = nn.Conv1d(
in_channels=d_model,
out_channels=d_model,
kernel_size=ker_size,
padding=ker_size - 1,
groups=d_model,
bias=True,
)
self.A = nn.Parameter(torch.arange(1, d_state + 1, dtype=torch.float).repeat(d_model, 1))
self.D = nn.Parameter(torch.ones(d_model, dtype=torch.float))
self.device = DEVICE
def forward(self, seq, cache=None):
b, l, d = seq.shape
(prev_hid, prev_inp) = cache if cache is not None else (None, None)
a, b = self.in_proj(seq).chunk(2, dim=-1)
x = rearrange(a, 'b l d -> b d l')
x = x if prev_inp is None else torch.cat((prev_inp, x), dim=-1)
a = self.conv(x)[..., :l]
a = rearrange(a, 'b d l -> b l d')
a = silu(a)
a, hid = self.ssm(a, prev_hid=prev_hid)
b = silu(b)
out = a * b
out = self.out_proj(out)
if cache:
cache = (hid.squeeze(), x[..., 1:])
return out, cache
def ssm(self, seq, prev_hid):
A = -self.A
D = +self.D
B = self.s_B(seq)
C = self.s_C(seq)
s = softplus(D + self.s_D(seq))
A_bar = einsum(torch.exp(A), s, 'd s, b l d -> b l d s')
B_bar = einsum( B, s, 'b l s, b l d -> b l d s')
X_bar = einsum(B_bar, seq, 'b l d s, b l d -> b l d s')
hid = self._hid_states(A_bar, X_bar, prev_hid=prev_hid)
out = einsum(hid, C, 'b l d s, b l s -> b l d')
out = out + D * seq
return out, hid
def _hid_states(self, A, X, prev_hid=None):
b, l, d, s = A.shape
A = rearrange(A, 'b l d s -> l b d s')
X = rearrange(X, 'b l d s -> l b d s')
if prev_hid is not None:
return rearrange(A * prev_hid + X, 'l b d s -> b l d s')
h = torch.zeros(b, d, s, device=self.device)
return torch.stack([h := A_t * h + X_t for A_t, X_t in zip(A, X)], dim=1)
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