"""
Feed-Forward Network for SLM.
Uses GELU activation (not SwiGLU) for better INT8 quantization.
"""

import torch
import torch.nn as nn
import torch.nn.functional as F

from .config import SLMConfig


class FeedForward(nn.Module):
    """Feed-Forward Network with GELU activation.

    Architecture: Linear -> GELU -> Linear
    - Input: [batch, seq, hidden_size=768]
    - Hidden: [batch, seq, intermediate_size=3072]
    - Output: [batch, seq, hidden_size=768]

    Why GELU over SwiGLU:
    - Fewer operations (2 matmuls vs 3)
    - Better INT8 quantization behavior
    - Full QNN support without decomposition
    - SwiGLU benefits mainly appear at >1B parameters
    """

    def __init__(self, config: SLMConfig):
        """Initialize FFN.

        Args:
            config: Model configuration
        """
        super().__init__()

        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size

        # Up projection: hidden -> intermediate
        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)

        # Down projection: intermediate -> hidden
        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)

        self.dropout = config.dropout

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Forward pass through FFN.

        Args:
            x: Input tensor [batch, seq, hidden_size]

        Returns:
            Output tensor [batch, seq, hidden_size]
        """
        # Up project and apply GELU
        hidden = self.up_proj(x)
        hidden = F.gelu(hidden, approximate="tanh")

        # Down project
        output = self.down_proj(hidden)

        # Apply dropout during training
        if self.training and self.dropout > 0:
            output = F.dropout(output, p=self.dropout)

        return output