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import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleP2GRMSNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
def forward(self, hidden_states):
input_dtype = hidden_states.dtype
hidden_states = hidden_states.to(torch.float32)
variance = hidden_states.pow(2).mean(-1, keepdim=True)
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
return self.weight * hidden_states.to(input_dtype)
class SimpleP2GMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.intermediate_size = getattr(
config,
"intermediate_size",
int(config.hidden_size * getattr(config, "mlp_ratio", 4.0)),
)
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
self.act_fn = nn.GELU(approximate="tanh")
self.dropout = nn.Dropout(getattr(config, "dropout", 0.0))
def forward(self, x):
down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
return self.dropout(down_proj)
class SimpleP2GAttention(nn.Module):
"""
multi-head self-attention module using F.scaled_dot_product_attention.
"""
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_heads
self.head_dim = self.hidden_size // self.num_heads
if self.hidden_size % self.num_heads != 0:
raise ValueError(f"hidden_size {self.hidden_size} must be divisible by num_heads {self.num_heads}")
self.attention_dropout = getattr(config, "attention_dropout", 0.0)
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
batch_size, seq_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = query_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
# Prepare attention mask for F.scaled_dot_product_attention
# F.sdpa expects mask to be True for positions that should attend
if attention_mask is not None:
if attention_mask.dim() == 2: # (B, N)
# Convert to (B, 1, 1, N) and invert (0 -> False, 1 -> True)
attention_mask = attention_mask.bool()
attention_mask = attention_mask.unsqueeze(1).unsqueeze(1)
elif attention_mask.dim() == 3: # (B, 1, N)
attention_mask = attention_mask.bool()
attention_mask = attention_mask.unsqueeze(1)
elif attention_mask.dim() == 4: # (B, 1, 1, N) or (B, H, N, N)
attention_mask = attention_mask.bool()
# Use F.scaled_dot_product_attention
attn_output = F.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=False,
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(batch_size, seq_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
return attn_output
class SimpleP2GCrossAttention(nn.Module):
"""
Multi-head cross-attention module.
"""
def __init__(self, config):
super().__init__()
self.config = config
self.hidden_size = config.hidden_size
self.num_heads = config.num_heads
self.head_dim = self.hidden_size // self.num_heads
if self.hidden_size % self.num_heads != 0:
raise ValueError(f"hidden_size {self.hidden_size} must be divisible by num_heads {self.num_heads}")
self.attention_dropout = getattr(config, "attention_dropout", 0.0)
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
def forward(
self,
hidden_states: torch.Tensor, # Query
encoder_hidden_states: torch.Tensor, # Key, Value
attention_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
batch_size, seq_len, _ = hidden_states.size()
encoder_seq_len = encoder_hidden_states.size(1)
query_states = self.q_proj(hidden_states)
key_states = self.k_proj(encoder_hidden_states)
value_states = self.v_proj(encoder_hidden_states)
query_states = query_states.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = key_states.view(batch_size, encoder_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = value_states.view(batch_size, encoder_seq_len, self.num_heads, self.head_dim).transpose(1, 2)
if attention_mask is not None and not (
attention_mask.is_floating_point() or attention_mask.dtype == torch.bool
):
attention_mask = attention_mask.bool()
attn_output = F.scaled_dot_product_attention(
query_states,
key_states,
value_states,
attn_mask=attention_mask,
dropout_p=self.attention_dropout if self.training else 0.0,
is_causal=False,
)
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.reshape(batch_size, seq_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
return attn_output
class SimpleP2GDecoderLayer(nn.Module):
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
eps = getattr(config, "rms_norm_eps", 1e-6)
# Layers
self.self_attn = SimpleP2GAttention(config=config)
self.cross_attn = SimpleP2GCrossAttention(config=config)
self.mlp = SimpleP2GMLP(config)
# Pre-Norms (Normalization happens BEFORE the sub-layer)
self.norm1 = SimpleP2GRMSNorm(self.hidden_size, eps=eps)
self.norm2 = SimpleP2GRMSNorm(self.hidden_size, eps=eps)
self.norm3 = SimpleP2GRMSNorm(self.hidden_size, eps=eps)
self.resid_dropout = nn.Dropout(getattr(config, "dropout", 0.0))
def forward(self, x, encoder_hidden_states, attention_mask=None, cross_attention_mask=None):
# 1. Self-Attention (Genes interacting with each other)
x = x + self.resid_dropout(self.self_attn(self.norm1(x), attention_mask=attention_mask))
# 2. Cross-Attention (Genes looking at the image context)
x = x + self.resid_dropout(
self.cross_attn(self.norm2(x), encoder_hidden_states, attention_mask=cross_attention_mask)
)
# 3. Feed-Forward
x = x + self.mlp(self.norm3(x))
return x
class SimpleP2G(nn.Module):
def __init__(
self,
config,
) -> None:
super().__init__()
self.config = config
self.decoder_depth = config.decoder_depth
self.num_genes = config.num_genes
self.hidden_size = config.hidden_size
self.gene_queries = nn.Embedding(self.num_genes, self.hidden_size)
self.cond_size = config.cond_size
self.num_cond_tokens = getattr(config, "num_cond_tokens", 1)
self.decoder_blocks = nn.ModuleList([SimpleP2GDecoderLayer(config) for _ in range(self.decoder_depth)])
self.final_norm = SimpleP2GRMSNorm(self.hidden_size)
self.mean_head = nn.Linear(self.hidden_size, 1)
if config.loss_name in ["gaussian_nll", "beta_nll"]:
self.log_var_head = nn.Linear(self.hidden_size, 1)
# if config.loss_name == "zinb":
# self.pi_head = nn.Linear(self.hidden_size, 1)
self.gradient_checkpointing = False
self.emb_dropout = nn.Dropout(getattr(config, "dropout", 0.0))
# ── Additional regularization ──
self.gene_drop_rate = float(getattr(config, "gene_drop_rate", 0.0))
self.emb_noise_std = float(getattr(config, "emb_noise_std", 0.0))
self.stochastic_depth_rate = float(getattr(config, "stochastic_depth_rate", 0.0))
self.label_embed = nn.Sequential(
nn.Linear(self.cond_size, self.cond_size, bias=True),
nn.SiLU(),
nn.Linear(self.cond_size, self.hidden_size * self.num_cond_tokens, bias=True),
)
def forward(self, c: torch.Tensor) -> tuple[torch.Tensor, ...]:
"""Forward pass."""
output = self.gene_queries.weight.unsqueeze(0).expand(c.size(0), -1, -1) # (B, num_genes, hidden_size)
output = self.emb_dropout(output)
# Gene dropout: randomly zero out gene query positions during training
if self.training and self.gene_drop_rate > 0:
gene_mask = torch.rand(c.size(0), self.num_genes, 1, device=c.device) > self.gene_drop_rate
output = output * gene_mask # zero out dropped genes
condition = self.label_embed(c).view(c.size(0), self.num_cond_tokens, self.hidden_size) # (B, T, hidden_size)
# Embedding noise: add Gaussian noise to conditioning tokens during training
if self.training and self.emb_noise_std > 0:
condition = condition + torch.randn_like(condition) * self.emb_noise_std
for i, block in enumerate(self.decoder_blocks):
# Stochastic depth: randomly skip layers during training
if self.training and self.stochastic_depth_rate > 0:
# Linear scaling: deeper layers have higher drop probability
drop_prob = self.stochastic_depth_rate * i / max(len(self.decoder_blocks) - 1, 1)
if torch.rand(1).item() < drop_prob:
continue
if self.gradient_checkpointing and self.training:
output = torch.utils.checkpoint.checkpoint(
block,
output,
encoder_hidden_states=condition,
use_reentrant=False,
)
else:
output = block(
output,
encoder_hidden_states=condition,
)
output = self.final_norm(output)
raw_mean = self.mean_head(output).squeeze(-1)
mean = F.softplus(raw_mean) # Ensures predicted G >= 0
if self.config.loss_name in ["gaussian_nll", "beta_nll"]:
log_var = self.log_var_head(output).squeeze(-1)
# if self.config.loss_name == "zinb":
# pi_logits = self.pi_head(output).squeeze(-1)
# return mean, log_var, pi_logits
elif self.config.loss_name in ["mse", "l1"]:
log_var = torch.zeros_like(mean)
return mean, log_var
def get_loss_fn(loss_type: str) -> nn.Module:
if loss_type == "gaussian_nll":
return GaussianNLLLoss()
elif loss_type == "beta_nll":
return BetaNLLLoss(beta=1.0)
elif loss_type == "mse":
return nn.MSELoss()
elif loss_type == "l1":
return nn.L1Loss()
else:
raise ValueError(f"Unsupported loss type: {loss_type}")
class GaussianNLLLoss(nn.Module):
def __init__(self, logvar_clipping=(-10, 10)):
super().__init__()
self.min_logvar, self.max_logvar = logvar_clipping
def forward(self, pred_mean, pred_logvar, target):
# 1. Clamp logvar to prevent NaN gradients or Inf precision
pred_logvar = torch.clamp(pred_logvar, self.min_logvar, self.max_logvar)
# 2. Precision = exp(-logvar) is 1/sigma^2
precision = torch.exp(-pred_logvar)
# 3. Standard NLL formula components
# (target - mean)^2 / variance + log(variance)
diff = (target - pred_mean) ** 2
loss = 0.5 * (precision * diff + pred_logvar)
return loss.mean()
class BetaNLLLoss(nn.Module):
"""
Beta-Negative Log-Likelihood Loss.
Reference: Seitzer et al., "Properties and Benefits of Training with
Beta-Negative Log-Likelihood", ICLR 2022.
"""
def __init__(self, beta=1.0, logvar_clipping=(-10, 10)):
super().__init__()
self.beta = beta
self.min_logvar, self.max_logvar = logvar_clipping
def forward(self, pred_mean, pred_logvar, target):
"""
pred_mean: (B, num_genes)
pred_logvar: (B, num_genes)
target: (B, num_genes)
"""
# 1. Clamp for stability
pred_logvar = torch.clamp(pred_logvar, self.min_logvar, self.max_logvar)
# 2. Calculate variance (sigma^2)
variance = torch.exp(pred_logvar)
# 3. Standard Gaussian NLL components
# (y - mu)^2 / sigma^2 + log(sigma^2)
squared_error = (target - pred_mean) ** 2
nll = (squared_error / variance) + pred_logvar
# 4. Apply Beta Weighting
# The 'stop_gradient' (detach) on the multiplier is optional
# but often used to stabilize the variance head.
# The paper version uses the variance gradient as well.
weight = variance.detach() ** self.beta
loss = 0.5 * weight * nll
return loss.mean()
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