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import torch
from torch import nn
import math
from transformers.models.whisper.modeling_whisper import WhisperAttention
from transformers.activations import ACT2FN
class CustomLinear(nn.Linear):
def __init__(self, *args, init_eye_val=0.0, fddt_init=None, init_fun=None, **kwargs):
super().__init__(*args, **kwargs)
self.init_eye_val = init_eye_val
self.fddt_init = fddt_init
self.init_fun = init_fun
self.reset_parameters() # Ensure consistent init on creation
def reset_parameters(self) -> None:
with torch.no_grad():
# Apply custom init function if provided
if hasattr(self,"init_fun") and self.init_fun is not None:
self.init_fun(self)
return
# Default initialization
nn.init.xavier_uniform_(self.weight)
if self.bias is not None:
nn.init.zeros_(self.bias)
if hasattr(self, "fddt_init"):
# FDDT-specific inits
if self.fddt_init == 'non-disturbing':
# Make weight an identity matrix (if possible)
if self.weight.shape[0] == self.weight.shape[1]:
self.weight.copy_(torch.eye(self.weight.shape[0], device=self.weight.device))
else:
# Not square — fill first min(n, m) diagonals
eye = torch.zeros_like(self.weight)
n = min(self.weight.shape)
eye[:n, :n] = torch.eye(n, device=self.weight.device)
self.weight.copy_(eye)
elif self.fddt_init == 'suppressive':
if self.weight.shape[0] == self.weight.shape[1]:
self.weight.copy_(self.init_eye_val * torch.eye(self.weight.shape[0], device=self.weight.device))
else:
eye = torch.zeros_like(self.weight)
n = min(self.weight.shape)
eye[:n, :n] = self.init_eye_val * torch.eye(n, device=self.weight.device)
self.weight.copy_(eye)
class CustomDiagonalLinear(nn.Module):
def __init__(self, d_model, bias=True, init_eye_val=0.0, fddt_init=None):
super().__init__()
self.init_eye_val = init_eye_val
self.weight = nn.Parameter(torch.full((d_model,), init_eye_val))
self.bias = nn.Parameter(torch.zeros(d_model)) if bias else None
self.fddt_init = fddt_init
self.reset_parameters()
def reset_parameters(self):
with torch.no_grad():
# random init
fan = self.weight.size(0)
bound = math.sqrt(3.0 / fan)
self.weight.uniform_(-bound, bound)
if self.bias is not None:
self.bias.zero_()
# custom modes
if self.fddt_init == 'non-disturbing':
self.weight.fill_(1.0)
elif self.fddt_init == 'suppressive':
self.weight.fill_(self.init_eye_val)
def forward(self, input):
out = input * self.weight
if self.bias is not None:
out += self.bias
return out
class Gate(nn.Module):
def __init__(self, items, init_val=0.0):
super().__init__()
self.init_val = init_val
self.gate = nn.Parameter(torch.full((items,), init_val))
self.reset_parameters()
def forward(self, orig_seq, new_seq):
gate_act = torch.nn.functional.tanh(self.gate)
output = orig_seq + gate_act * new_seq
return output
def reset_parameters(self):
with torch.no_grad():
self.gate.fill_(self.init_val)
def propagate_first_half_embeds_init(module):
# Zero out all weights initially
# module.weight.data.zero_()
torch.nn.init.xavier_uniform_(module.weight, gain=1e-1)
# Create identity mapping for first half of input (cross_attn_output)
# Input: [cross_attn_output, q_orig] -> map cross_attn_output to first embed_dim outputs
module.weight.data[:module.weight.shape[1] // 2, :module.weight.shape[1] // 2] += torch.eye(
module.weight.shape[1] // 2)
# Zero bias
module.bias.data.zero_()
def propage_first_embeds_to_match_output_dim_init(module):
# module.weight.data.zero_()
torch.nn.init.xavier_uniform_(module.weight, gain=1e-1)
# Create identity mapping from first embed_dim inputs to output
module.weight.data[:, :module.weight.shape[0]] += torch.eye(module.weight.shape[0])
# Zero bias for second linear
module.bias.data.zero_()
# Cross attention block that can easily learn to ignore cross attention initially
class CrossAttentionEnrollBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.embed_dim = config.d_model
self.ffn_dim = config.encoder_ffn_dim
self.cross_attn = WhisperAttention(
embed_dim=self.embed_dim,
num_heads=config.encoder_attention_heads,
dropout=config.attention_dropout,
config=config,
)
# Layer normalization (pre-norm style)
# self.norm_attn = nn.LayerNorm(self.embed_dim, eps=layer_norm_eps)
self.cross_gate = Gate(1,init_val=.0)
# Feed-forward network that maps concat space back to single channel
self.ffn = nn.Sequential(
CustomLinear(self.embed_dim * 2, self.ffn_dim, init_fun=propagate_first_half_embeds_init),
ACT2FN[config.activation_function],
nn.Dropout(config.dropout if hasattr(config, 'dropout') else 0.1),
CustomLinear(self.ffn_dim, self.embed_dim, init_fun=propage_first_embeds_to_match_output_dim_init),
nn.Dropout(config.dropout if hasattr(config, 'dropout') else 0.1)
)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
"""
Args:
hidden_states: (B, 2, T, F) - batch, channels, time, features
Returns:
Updated hidden states of same shape
"""
q = hidden_states[:, 0] # (B, T, F)
kv = hidden_states[:, 1] # (B, T, F)
# Cross-attention
attn_output = self.cross_attn(
hidden_states=q,
key_value_states=kv,
output_attentions=False
)[0]
# Concatenate attention output with original normalized query
q_concat = torch.cat([attn_output, q], dim=-1) # (B, T, 2*F)
# Feed-forward processing (no normalization to preserve initialization)
updated_q = self.ffn(q_concat) # (B, T, F)
q_out = self.cross_gate(q, updated_q)
# Return stacked result (only query channel is updated)
return torch.stack([q_out, kv], dim=1)
class SpeakerCommunicationBlock(nn.Module):
def __init__(self, config):
super().__init__()
self.streams = 2
self.config = config
self.cae = CrossAttentionEnrollBlock(config)
def forward(self, x):
# x: (B, T, F)
B, T, F = x.shape
S = self.streams
# Reshape to (B//S, S, T, F)
x_reshaped = x.view(B//S, S, T, F)
# Call the selected method
out = self.cae(x_reshaped)
# Reshape back (B, T, F)
out_merged = out.view(B, T, F)
return out_merged
if __name__ == "__main__":
model1 = CustomLinear(16 * 2, 64, init_fun=propagate_first_half_embeds_init)
model2 = CustomLinear(64, 16, init_fun=propage_first_embeds_to_match_output_dim_init)
input1 = torch.ones(16, 16)
input2 = torch.zeros(16, 16)
input = torch.concat((input1, input2), dim=-1)
output = model2(model1(input))
print(f"Mean err: {(input1-output).mean()}")
model_1 = CustomDiagonalLinear(4, bias=False, fddt_init='suppressive', init_eye_val=0.1)
model_2 = CustomDiagonalLinear(4, bias=False, fddt_init='suppressive', init_eye_val=0.1)
model_3 = CustomDiagonalLinear(4, bias=False, fddt_init='suppressive', init_eye_val=0.1)
model_4 = CustomDiagonalLinear(4, bias=False, fddt_init='suppressive', init_eye_val=0.1)
model = nn.Sequential(model_1, model_2, model_3, model_4)
opt = torch.optim.Adam(model.parameters(), lr=0.01)
model_1.reset_parameters()
x = torch.ones(2, 4)
y = torch.ones(2, 4)
for i in range(100):
opt.zero_grad()
loss = ((model(x) - y) ** 2).mean()
loss.backward()
opt.step()
print(f"Step {i}: mean weight {model_1.weight.mean().item():.4f}") |