3d2da94ce2
- Complete transformer implementation from scratch - Training pipeline with gradient accumulation and mixed precision - Optimized inference with KV caching - Multi-format data processing (PDFs, images, code, text) - Comprehensive documentation - Apache 2.0 license - Example training plots included in docs/images/
221 lines
7.2 KiB
Python
221 lines
7.2 KiB
Python
"""
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Multi-Head Attention mechanism from "Attention Is All You Need"
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Includes optimizations for long context and hallucination reduction
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import math
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from typing import Optional, Tuple
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class MultiHeadAttention(nn.Module):
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"""
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Multi-Head Attention mechanism with optional causal masking.
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Features:
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- Scaled dot-product attention
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- Optional causal masking for autoregressive generation
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- Efficient attention computation
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"""
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def __init__(
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self,
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d_model: int,
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num_heads: int,
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dropout: float = 0.1,
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bias: bool = False,
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causal: bool = False,
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):
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"""
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Args:
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d_model: Model dimension
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num_heads: Number of attention heads
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dropout: Dropout probability
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bias: Whether to use bias in linear layers
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causal: Whether to use causal masking
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"""
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super().__init__()
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assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
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self.d_model = d_model
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self.num_heads = num_heads
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self.d_k = d_model // num_heads
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self.causal = causal
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# Linear projections for Q, K, V
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self.q_proj = nn.Linear(d_model, d_model, bias=bias)
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self.k_proj = nn.Linear(d_model, d_model, bias=bias)
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self.v_proj = nn.Linear(d_model, d_model, bias=bias)
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self.out_proj = nn.Linear(d_model, d_model, bias=bias)
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self.dropout = nn.Dropout(dropout)
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self.scale = 1.0 / math.sqrt(self.d_k)
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def forward(
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self,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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mask: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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"""
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Forward pass of multi-head attention.
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Args:
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query: Query tensor [batch_size, seq_len, d_model]
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key: Key tensor [batch_size, seq_len, d_model]
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value: Value tensor [batch_size, seq_len, d_model]
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mask: Optional attention mask [batch_size, seq_len, seq_len]
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Returns:
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output: Attention output [batch_size, seq_len, d_model]
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attention_weights: Attention weights [batch_size, num_heads, seq_len, seq_len]
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"""
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batch_size, seq_len, _ = query.shape
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# Project Q, K, V
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Q = self.q_proj(query) # [batch_size, seq_len, d_model]
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K = self.k_proj(key)
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V = self.v_proj(value)
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# Reshape for multi-head attention
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Q = Q.view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2) # [batch_size, num_heads, seq_len, d_k]
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K = K.view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)
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V = V.view(batch_size, seq_len, self.num_heads, self.d_k).transpose(1, 2)
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# Compute attention scores
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scores = torch.matmul(Q, K.transpose(-2, -1)) * self.scale # [batch_size, num_heads, seq_len, seq_len]
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# Apply causal mask if needed
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if self.causal:
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causal_mask = torch.triu(
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torch.ones(seq_len, seq_len, device=query.device, dtype=torch.bool),
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diagonal=1
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)
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scores.masked_fill_(causal_mask, float('-inf'))
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# Apply external mask if provided
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if mask is not None:
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scores.masked_fill_(mask.unsqueeze(1) == 0, float('-inf'))
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# Compute attention weights
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attention_weights = F.softmax(scores, dim=-1)
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attention_weights = self.dropout(attention_weights)
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# Apply attention to values
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output = torch.matmul(attention_weights, V) # [batch_size, num_heads, seq_len, d_k]
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# Concatenate heads
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output = output.transpose(1, 2).contiguous() # [batch_size, seq_len, num_heads, d_k]
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output = output.view(batch_size, seq_len, self.d_model) # [batch_size, seq_len, d_model]
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# Final projection
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output = self.out_proj(output)
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return output, attention_weights
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class PositionalEncoding(nn.Module):
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"""
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Positional encoding for transformer models.
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Uses sinusoidal positional encoding as described in "Attention Is All You Need".
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"""
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def __init__(self, d_model: int, max_len: int = 5000, dropout: float = 0.1):
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"""
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Args:
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d_model: Model dimension
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max_len: Maximum sequence length
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dropout: Dropout probability
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"""
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super().__init__()
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self.dropout = nn.Dropout(p=dropout)
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# Create positional encoding matrix
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pe = torch.zeros(max_len, d_model)
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position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
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div_term = torch.exp(
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torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)
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)
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pe[:, 0::2] = torch.sin(position * div_term)
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pe[:, 1::2] = torch.cos(position * div_term)
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pe = pe.unsqueeze(0) # [1, max_len, d_model]
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# Register as buffer (not a parameter)
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self.register_buffer('pe', pe)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Add positional encoding to input.
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Args:
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x: Input tensor [batch_size, seq_len, d_model]
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Returns:
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Output with positional encoding added
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"""
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seq_len = x.shape[1]
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x = x + self.pe[:, :seq_len, :]
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return self.dropout(x)
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class RotaryPositionalEncoding(nn.Module):
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"""
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Rotary Position Embedding (RoPE) - More efficient for long sequences.
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Better for long-horizon execution tasks.
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"""
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def __init__(self, d_model: int, max_len: int = 8192):
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"""
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Args:
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d_model: Model dimension (must be even)
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max_len: Maximum sequence length
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"""
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super().__init__()
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assert d_model % 2 == 0, "d_model must be even for RoPE"
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self.d_model = d_model
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self.max_len = max_len
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# Precompute frequency matrix
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inv_freq = 1.0 / (10000 ** (torch.arange(0, d_model, 2).float() / d_model))
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self.register_buffer('inv_freq', inv_freq)
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def forward(self, x: torch.Tensor, offset: int = 0) -> torch.Tensor:
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"""
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Apply rotary positional encoding.
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Args:
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x: Input tensor [batch_size, seq_len, d_model]
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offset: Position offset for relative positions
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Returns:
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Rotated input tensor
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"""
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seq_len = x.shape[1]
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device = x.device
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# Generate position indices
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t = torch.arange(offset, offset + seq_len, device=device).type_as(self.inv_freq)
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freqs = torch.outer(t, self.inv_freq)
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emb = torch.cat((freqs, freqs), dim=-1)
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# Apply rotation
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cos = emb.cos()
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sin = emb.sin()
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# Split input into two halves
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x1, x2 = x.chunk(2, dim=-1)
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# Apply rotation
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rotated = torch.cat([
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x1 * cos - x2 * sin,
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x1 * sin + x2 * cos
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], dim=-1)
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return rotated
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