Initial commit: SheepOp LLM - Transformer-based language model implementation
- 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/
This commit is contained in:
Carlos Gutierrez
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models/optimized_attention.py
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413
models/optimized_attention.py
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"""
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Optimized attention mechanisms for production RAG systems
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Implements KV caching, optimized attention computation, and retrieval optimizations
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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, Dict, List
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from dataclasses import dataclass
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@dataclass
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class KVCache:
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"""Key-Value cache for efficient autoregressive generation."""
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keys: torch.Tensor # [batch_size, num_heads, seq_len, d_k]
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values: torch.Tensor # [batch_size, num_heads, seq_len, d_k]
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def append(self, new_keys: torch.Tensor, new_values: torch.Tensor):
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"""Append new keys and values to the cache."""
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self.keys = torch.cat([self.keys, new_keys], dim=2)
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self.values = torch.cat([self.values, new_values], dim=2)
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def clear(self):
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"""Clear the cache."""
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self.keys = None
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self.values = None
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class OptimizedMultiHeadAttention(nn.Module):
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"""
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Optimized Multi-Head Attention with KV caching and efficient computation.
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Features:
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- KV cache for autoregressive generation
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- Optimized attention computation
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- Support for incremental decoding
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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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use_flash_attention: 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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use_flash_attention: Whether to use optimized flash attention (if available)
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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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self.use_flash_attention = use_flash_attention
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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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# KV cache for inference
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self.kv_cache: Optional[KVCache] = None
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def forward(
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self,
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query: torch.Tensor,
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key: Optional[torch.Tensor] = None,
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value: Optional[torch.Tensor] = None,
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mask: Optional[torch.Tensor] = None,
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use_cache: bool = False,
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cache_position: Optional[int] = None,
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) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
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"""
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Forward pass with optional KV caching.
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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] (if None, uses query)
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value: Value tensor [batch_size, seq_len, d_model] (if None, uses query)
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mask: Optional attention mask [batch_size, seq_len, seq_len]
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use_cache: Whether to use KV cache
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cache_position: Position in cache for incremental decoding
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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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if key is None:
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key = query
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if value is None:
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value = query
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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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# Use KV cache if available and enabled
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if use_cache and self.kv_cache is not None:
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# Append new keys and values to cache
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self.kv_cache.append(K, V)
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K = self.kv_cache.keys
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V = self.kv_cache.values
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kv_seq_len = K.shape[2]
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else:
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kv_seq_len = seq_len
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# Compute attention scores with optimized computation
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if self.use_flash_attention and hasattr(F, 'scaled_dot_product_attention'):
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# Use PyTorch's optimized scaled dot product attention
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output = F.scaled_dot_product_attention(
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Q, K, V,
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attn_mask=mask,
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dropout_p=self.dropout.p if self.training else 0.0,
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is_causal=self.causal,
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)
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attention_weights = None # Flash attention doesn't return weights
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else:
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# Standard attention computation
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scores = torch.matmul(Q, K.transpose(-2, -1)) * self.scale # [batch_size, num_heads, seq_len, kv_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, kv_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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if mask.dim() == 2:
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mask = mask.unsqueeze(1).unsqueeze(1) # [batch_size, 1, seq_len, kv_seq_len]
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scores.masked_fill_(mask == 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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def init_kv_cache(self, batch_size: int, max_length: int, device: torch.device):
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"""Initialize KV cache for inference."""
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self.kv_cache = KVCache(
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keys=torch.empty(batch_size, self.num_heads, 0, self.d_k, device=device),
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values=torch.empty(batch_size, self.num_heads, 0, self.d_k, device=device),
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)
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def clear_cache(self):
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"""Clear the KV cache."""
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self.kv_cache = None
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class RetrievalCache:
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"""
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Approximate cache for retrieval results.
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Reduces expensive vector database lookups by caching similar queries.
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"""
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def __init__(self, max_size: int = 1000, similarity_threshold: float = 0.9):
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"""
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Args:
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max_size: Maximum number of cached entries
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similarity_threshold: Minimum similarity to consider a cache hit
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"""
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self.max_size = max_size
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self.similarity_threshold = similarity_threshold
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self.cache: Dict[str, List[Dict]] = {} # query_hash -> retrieved_docs
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self.query_embeddings: Dict[str, torch.Tensor] = {} # query_hash -> embedding
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def get(self, query_hash: str, query_embedding: torch.Tensor) -> Optional[List[Dict]]:
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"""
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Retrieve cached results if similar query exists.
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Args:
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query_hash: Hash of the query
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query_embedding: Embedding of the query
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Returns:
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Cached results if found, None otherwise
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"""
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# Check exact match first
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if query_hash in self.cache:
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return self.cache[query_hash]
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# Check for similar queries
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best_match = None
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best_similarity = 0.0
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for cached_hash, cached_embedding in self.query_embeddings.items():
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# Compute cosine similarity
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similarity = F.cosine_similarity(
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query_embedding.unsqueeze(0),
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cached_embedding.unsqueeze(0)
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).item()
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if similarity > best_similarity:
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best_similarity = similarity
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best_match = cached_hash
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if best_similarity >= self.similarity_threshold and best_match:
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return self.cache[best_match]
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return None
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def set(self, query_hash: str, query_embedding: torch.Tensor, results: List[Dict]):
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"""
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Store query and results in cache.
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Args:
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query_hash: Hash of the query
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query_embedding: Embedding of the query
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results: Retrieved documents/results
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"""
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# Remove oldest entry if cache is full
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if len(self.cache) >= self.max_size:
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oldest_key = next(iter(self.cache))
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del self.cache[oldest_key]
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del self.query_embeddings[oldest_key]
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self.cache[query_hash] = results
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self.query_embeddings[query_hash] = query_embedding
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def clear(self):
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"""Clear the cache."""
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self.cache.clear()
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self.query_embeddings.clear()
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class OptimizedInference:
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"""
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Optimized inference utilities for production RAG systems.
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Includes prefetching, batching, and parallel processing.
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"""
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def __init__(self, model: nn.Module, device: torch.device):
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"""
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Args:
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model: Model to use for inference
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device: Device to run inference on
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"""
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self.model = model
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self.device = device
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self.model.eval()
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@torch.no_grad()
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def generate_with_cache(
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self,
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input_ids: torch.Tensor,
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max_length: int = 100,
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temperature: float = 1.0,
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top_k: Optional[int] = None,
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top_p: float = 1.0,
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do_sample: bool = True,
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) -> torch.Tensor:
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"""
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Generate with KV cache for efficient autoregressive generation.
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Args:
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input_ids: Starting token indices [batch_size, seq_len]
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max_length: Maximum generation length
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temperature: Sampling temperature
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top_k: Top-k sampling parameter
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top_p: Nucleus sampling parameter
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do_sample: Whether to sample or use greedy decoding
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Returns:
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Generated token sequences
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"""
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batch_size = input_ids.shape[0]
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device = input_ids.device
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# Initialize KV cache in all attention layers
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for module in self.model.modules():
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if isinstance(module, OptimizedMultiHeadAttention):
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module.init_kv_cache(batch_size, max_length, device)
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generated = input_ids.clone()
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for _ in range(max_length - input_ids.shape[1]):
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# Forward pass
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logits, _ = self.model(generated)
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next_token_logits = logits[:, -1, :] / temperature
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# Apply top-k filtering
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if top_k is not None and top_k > 0:
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indices_to_remove = next_token_logits < torch.topk(next_token_logits, top_k)[0][..., -1, None]
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next_token_logits[indices_to_remove] = float('-inf')
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# Apply top-p (nucleus) filtering
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if top_p < 1.0:
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sorted_logits, sorted_indices = torch.sort(next_token_logits, descending=True)
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cumulative_probs = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1)
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sorted_indices_to_remove = cumulative_probs > top_p
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sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
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sorted_indices_to_remove[..., 0] = 0
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indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
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next_token_logits[indices_to_remove] = float('-inf')
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# Sample or take argmax
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if do_sample:
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probs = torch.softmax(next_token_logits, dim=-1)
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next_token = torch.multinomial(probs, num_samples=1)
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else:
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next_token = torch.argmax(next_token_logits, dim=-1, keepdim=True)
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# Early stopping: stop if EOS or padding token is generated (for batch_size=1)
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if batch_size == 1:
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eos_token_id = 3 # Default EOS token ID
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if next_token.item() == eos_token_id:
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break
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# Early stopping: stop if padding token is generated (prevent generating padding)
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pad_token_id = 0 # Default padding token ID
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if next_token.item() == pad_token_id:
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break
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# Append to generated sequence
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generated = torch.cat([generated, next_token], dim=1)
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# Clear KV cache
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for module in self.model.modules():
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if isinstance(module, OptimizedMultiHeadAttention):
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module.clear_cache()
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return generated
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@torch.no_grad()
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def batch_generate(
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self,
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input_ids_list: List[torch.Tensor],
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max_length: int = 100,
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temperature: float = 1.0,
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top_k: Optional[int] = None,
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top_p: float = 1.0,
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batch_size: int = 8,
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) -> List[torch.Tensor]:
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"""
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Generate for multiple prompts in batches for efficiency.
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Args:
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input_ids_list: List of starting token sequences
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max_length: Maximum generation length
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temperature: Sampling temperature
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top_k: Top-k sampling parameter
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top_p: Nucleus sampling parameter
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batch_size: Batch size for processing
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Returns:
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List of generated sequences
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"""
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results = []
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for i in range(0, len(input_ids_list), batch_size):
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batch = input_ids_list[i:i + batch_size]
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# Pad to same length
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max_len = max(seq.shape[1] for seq in batch)
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padded_batch = []
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for seq in batch:
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padding = torch.zeros(seq.shape[0], max_len - seq.shape[1],
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dtype=seq.dtype, device=seq.device)
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padded_batch.append(torch.cat([seq, padding], dim=1))
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batch_tensor = torch.cat(padded_batch, dim=0)
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# Generate for batch
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generated = self.generate_with_cache(
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batch_tensor,
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max_length=max_length,
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temperature=temperature,
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top_k=top_k,
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top_p=top_p,
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)
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results.extend([gen for gen in generated])
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return results
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