mirror of
https://github.com/MLSysBook/TinyTorch.git
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96880b3133
Re-exported all modules after restructuring: - Updated _modidx.py with new module locations - Removed outdated autogeneration headers - Updated all core modules (tensor, autograd, layers, etc.) - Updated optimization modules (quantization, compression, etc.) - Updated TITO commands for new structure Changes include: - 24 tinytorch/ module files - 24 tito/ command and core files - Updated references from modules/source/ to modules/ All modules re-exported via nbdev from their new locations.
665 lines
30 KiB
Python
Generated
665 lines
30 KiB
Python
Generated
# AUTOGENERATED! DO NOT EDIT! File to edit: ../../modules/source/15_memoization/memoization_dev.ipynb.
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# %% auto 0
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__all__ = ['KVCache', 'enable_kv_cache', 'disable_kv_cache']
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# %% ../../modules/source/15_memoization/memoization_dev.ipynb 1
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import numpy as np
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import time
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from typing import Tuple, Optional, Dict, List
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# Import TinyTorch components from previous modules
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from ..core.tensor import Tensor
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# %% ../../modules/source/15_memoization/memoization_dev.ipynb 7
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class KVCache:
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"""
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Efficient key-value cache for autoregressive generation.
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Stores K,V matrices for each transformer layer to avoid recomputation
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during sequential token generation. This is THE critical optimization
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that makes production language model serving economically viable.
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⚠️ IMPORTANT: INFERENCE-ONLY (No Gradient Tracking)
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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KV caching is designed ONLY for inference (generation), NOT training.
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- During generation: No gradients computed (model.eval() mode)
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- Cache operations use .data (no gradient tracking)
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- This is correct and intentional for maximum speed
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- DO NOT use caching during training (use standard forward pass)
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Architecture:
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- Pre-allocates cache tensors with maximum sequence length
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- Tracks current sequence position for efficient O(1) updates
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- Provides update() method to append new K,V pairs without copying
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- Provides get() method to retrieve cached values for attention
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- Handles multiple layers and attention heads properly
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Memory Layout:
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```
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Layer 0: [Key_cache, Value_cache] # Shape: (batch, num_heads, max_seq, head_dim)
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Layer 1: [Key_cache, Value_cache]
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...
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Layer N: [Key_cache, Value_cache]
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```
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Performance:
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- Update: O(1) - just index assignment
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- Get: O(1) - just slicing (no data copy)
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- Memory: O(num_layers × batch × heads × max_seq × head_dim)
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"""
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def __init__(self, batch_size: int, max_seq_len: int, num_layers: int,
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num_heads: int, head_dim: int):
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"""
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Initialize KV cache for efficient generation.
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TODO: Set up pre-allocated cache storage for all transformer layers
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APPROACH:
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1. Store configuration parameters (batch_size, max_seq_len, etc.)
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2. Initialize sequence position counter to 0
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3. Create empty list for cache storage
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4. For each layer, pre-allocate zero-filled key and value caches
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5. Store each layer's (key_cache, value_cache) tuple in the list
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Args:
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batch_size: Number of sequences to generate simultaneously
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max_seq_len: Maximum sequence length to support
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num_layers: Number of transformer layers
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num_heads: Number of attention heads per layer
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head_dim: Dimension of each attention head
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EXAMPLE:
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>>> cache = KVCache(batch_size=2, max_seq_len=128, num_layers=4,
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... num_heads=8, head_dim=64)
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>>> cache.seq_pos # 0 (no tokens cached yet)
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>>> len(cache.caches) # 4 (one per layer)
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>>> cache.caches[0][0].shape # (2, 8, 128, 64) - key cache for layer 0
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HINTS:
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- Cache shape: (batch_size, num_heads, max_seq_len, head_dim)
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- Use Tensor(np.zeros(...)) to create cache tensors
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- Store caches as list of tuples: [(key_0, val_0), (key_1, val_1), ...]
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- Pre-allocation avoids dynamic resizing overhead during generation
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"""
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### BEGIN SOLUTION
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self.batch_size = batch_size
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self.max_seq_len = max_seq_len
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self.num_layers = num_layers
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self.num_heads = num_heads
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self.head_dim = head_dim
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# Current sequence position (how many tokens are cached)
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self.seq_pos = 0
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# Cache storage: list of (key_cache, value_cache) tuples per layer
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self.caches = []
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for layer_idx in range(num_layers):
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# Pre-allocate cache tensors with maximum size
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# Shape: (batch_size, num_heads, max_seq_len, head_dim)
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key_cache = Tensor(np.zeros((batch_size, num_heads, max_seq_len, head_dim)))
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value_cache = Tensor(np.zeros((batch_size, num_heads, max_seq_len, head_dim)))
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self.caches.append((key_cache, value_cache))
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### END SOLUTION
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def update(self, layer_idx: int, key: Tensor, value: Tensor) -> None:
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"""
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Update cache with new key-value pairs for given layer.
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TODO: Efficiently append new K,V to cache without data copying
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APPROACH:
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1. Validate layer_idx is in range [0, num_layers-1]
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2. Validate seq_pos hasn't exceeded max_seq_len
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3. Retrieve the (key_cache, value_cache) tuple for this layer
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4. Write new key to position seq_pos in key_cache using indexed assignment
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5. Write new value to position seq_pos in value_cache using indexed assignment
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6. Note: seq_pos is advanced externally via advance() after all layers
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This is the core caching operation - efficiently append new K,V
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to the cache without recomputation. This operation is O(1) because
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it's just an indexed assignment.
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IMPORTANT: KV caching is designed for INFERENCE (generation) only,
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not training. During generation, gradients are not computed. If you
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need gradients, don't use caching (use standard forward pass instead).
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Args:
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layer_idx: Which transformer layer (0 to num_layers-1)
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key: New key tensor, shape (batch_size, num_heads, 1, head_dim)
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value: New value tensor, shape (batch_size, num_heads, 1, head_dim)
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EXAMPLE:
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>>> cache = KVCache(batch_size=1, max_seq_len=10, num_layers=2,
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... num_heads=4, head_dim=64)
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>>> new_k = Tensor(np.random.randn(1, 4, 1, 64))
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>>> new_v = Tensor(np.random.randn(1, 4, 1, 64))
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>>> cache.update(layer_idx=0, key=new_k, value=new_v)
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>>> cache.seq_pos # Still 0 (update doesn't advance position)
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>>> cache.advance()
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>>> cache.seq_pos # Now 1
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HINTS:
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- Use slicing: cache[:, :, seq_pos:seq_pos+1, :] to write to position
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- Use .data for direct NumPy access (no gradient tracking needed)
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- Raise ValueError with helpful messages for invalid inputs
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- This is an in-place operation (modifies cache, returns None)
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Raises:
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ValueError: If layer_idx is out of range or sequence is full
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"""
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### BEGIN SOLUTION
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if layer_idx >= self.num_layers:
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raise ValueError(f"Layer index {layer_idx} >= num_layers {self.num_layers}")
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if self.seq_pos >= self.max_seq_len:
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raise ValueError(f"Sequence position {self.seq_pos} >= max_seq_len {self.max_seq_len}")
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# Get cache for this layer
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key_cache, value_cache = self.caches[layer_idx]
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# Update cache at current position (efficient O(1) write)
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# Note: We use .data here because caching is inference-only (no gradients needed)
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# This avoids gradient tracking overhead during generation
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key_cache.data[:, :, self.seq_pos:self.seq_pos+1, :] = key.data
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value_cache.data[:, :, self.seq_pos:self.seq_pos+1, :] = value.data
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# Note: seq_pos is advanced externally via advance() after all layers process
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### END SOLUTION
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def get(self, layer_idx: int) -> Tuple[Tensor, Tensor]:
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"""
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Retrieve cached key-value pairs for attention computation.
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TODO: Return only the valid cached portion for this layer
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APPROACH:
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1. Validate layer_idx is in range
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2. Retrieve the (key_cache, value_cache) tuple for this layer
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3. Calculate valid_len = seq_pos (number of tokens currently cached)
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4. Slice key_cache to get [:, :, :valid_len, :] (only filled portion)
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5. Slice value_cache to get [:, :, :valid_len, :] (only filled portion)
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6. Wrap sliced data in new Tensor objects and return
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Returns only the valid portion of the cache (up to current seq_pos).
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This is O(1) because we're just slicing NumPy arrays (view, not copy).
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IMPORTANT: Returns Tensors without gradient tracking since caching
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is inference-only. The returned tensors can be used in attention
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computation but won't propagate gradients backward.
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Args:
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layer_idx: Which transformer layer to get cache for
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Returns:
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(cached_keys, cached_values): Tensors shaped for attention
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Keys: (batch_size, num_heads, seq_pos, head_dim)
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Values: (batch_size, num_heads, seq_pos, head_dim)
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EXAMPLE:
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>>> cache = KVCache(batch_size=1, max_seq_len=100, num_layers=2,
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... num_heads=4, head_dim=64)
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>>> # After processing 3 tokens
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>>> cache.seq_pos = 3
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>>> cached_k, cached_v = cache.get(layer_idx=0)
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>>> cached_k.shape # (1, 4, 3, 64) - only first 3 positions
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>>> cached_v.shape # (1, 4, 3, 64)
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HINTS:
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- valid_len = self.seq_pos (how many tokens have been cached so far)
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- Use slicing: cache.data[:, :, :valid_len, :] to get valid portion
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- Wrap result in Tensor() for consistency with TinyTorch API
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- If seq_pos=0, returns empty cache (shape with 0 in sequence dimension)
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Raises:
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ValueError: If layer_idx is out of range
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"""
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### BEGIN SOLUTION
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if layer_idx >= self.num_layers:
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raise ValueError(f"Layer index {layer_idx} >= num_layers {self.num_layers}")
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# Get cache for this layer
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key_cache, value_cache = self.caches[layer_idx]
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# Return only the valid portion (up to current sequence position)
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# seq_pos tracks where to write next, so we have seq_pos valid tokens
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valid_len = self.seq_pos
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# Note: Creating new Tensors from .data (no gradient tracking)
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# This is correct for inference-only caching
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cached_keys = Tensor(key_cache.data[:, :, :valid_len, :])
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cached_values = Tensor(value_cache.data[:, :, :valid_len, :])
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return cached_keys, cached_values
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### END SOLUTION
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def advance(self) -> None:
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"""
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Advance sequence position after processing current token.
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Call this after all layers have processed the current token and
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updated their caches. This moves the write pointer forward.
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"""
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self.seq_pos += 1
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def reset(self) -> None:
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"""
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Reset cache for new generation sequence.
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Call this when starting a new generation (new prompt).
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Resets the sequence position counter and optionally zeros cache data.
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"""
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self.seq_pos = 0
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# Zero out caches for clean state (helps with debugging)
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for layer_idx in range(self.num_layers):
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key_cache, value_cache = self.caches[layer_idx]
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key_cache.data.fill(0.0)
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value_cache.data.fill(0.0)
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def get_memory_usage(self) -> Dict[str, float]:
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"""
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Calculate memory usage of the cache system.
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Returns:
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Dictionary with memory statistics in MB
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"""
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# Calculate size of one cache tensor
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cache_size = self.batch_size * self.num_heads * self.max_seq_len * self.head_dim
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bytes_per_float = 4 # float32
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# Each layer has key_cache + value_cache
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total_cache_tensors = self.num_layers * 2
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total_elements = cache_size * total_cache_tensors
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total_bytes = total_elements * bytes_per_float
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total_mb = total_bytes / (1024 * 1024)
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return {
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'total_mb': total_mb,
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'per_layer_mb': total_mb / self.num_layers,
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'cache_tensors': total_cache_tensors,
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'total_elements': total_elements
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}
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# %% ../../modules/source/15_memoization/memoization_dev.ipynb 11
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def enable_kv_cache(batch_size: int, max_seq_len: int, num_layers: int,
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num_heads: int, head_dim: int) -> KVCache:
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"""
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Create and return a KVCache instance for model generation.
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This function creates a properly sized cache for the model architecture.
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Call this before starting generation, then pass the cache to your
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generation loop.
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Args:
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batch_size: Number of sequences to generate simultaneously
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max_seq_len: Maximum sequence length to support
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num_layers: Number of transformer layers in model
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num_heads: Number of attention heads per layer
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head_dim: Dimension per attention head (usually embed_dim // num_heads)
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Returns:
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KVCache instance ready for use
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Example:
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```python
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# Enable caching for generation
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cache = enable_kv_cache(
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batch_size=1,
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max_seq_len=100,
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num_layers=4,
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num_heads=4,
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head_dim=32
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)
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# Use in generation loop (pseudocode)
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for step in range(max_new_tokens):
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# Only process new token with cache
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logits = model.forward_cached(new_token, cache)
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next_token = sample(logits)
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```
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"""
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cache = KVCache(batch_size, max_seq_len, num_layers, num_heads, head_dim)
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print(f"⚡ KV Cache enabled:")
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print(f" Batch size: {batch_size}")
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print(f" Max sequence: {max_seq_len}")
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print(f" Layers: {num_layers}")
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print(f" Heads: {num_heads}")
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print(f" Head dim: {head_dim}")
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mem_info = cache.get_memory_usage()
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print(f" Memory: {mem_info['total_mb']:.2f} MB")
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print()
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return cache
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# %% ../../modules/source/15_memoization/memoization_dev.ipynb 16
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def enable_kv_cache(model):
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"""
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Enable KV caching for a transformer model WITHOUT modifying Module 12/13 code.
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TODO: Create cache and non-invasively patch attention layers
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APPROACH:
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1. Validate model has required attributes (embed_dim, num_layers, num_heads, max_seq_len, blocks)
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2. Calculate head_dim from embed_dim and num_heads
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3. Create KVCache instance sized for this model's architecture
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4. Store cache on model as model._kv_cache and set model._cache_enabled flag
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5. For each transformer block, wrap its attention forward method with caching logic
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6. Print confirmation message with cache statistics
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7. Return the cache object
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This function demonstrates **non-invasive optimization** - adding capabilities
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to existing systems without breaking them. Similar to how Module 05 (Autograd)
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uses enable_autograd() to add gradient tracking to Tensors.
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Args:
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model: A GPT-style transformer model with:
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- model.embed_dim (int)
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- model.num_layers (int)
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- model.num_heads (int)
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- model.max_seq_len (int)
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- model.blocks (list of TransformerBlock objects)
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Returns:
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cache: KVCache object for this model
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EXAMPLE:
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>>> from tinytorch.models.transformer import GPT
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>>> model = GPT(vocab_size=100, embed_dim=128, num_layers=4, num_heads=4)
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>>> cache = enable_kv_cache(model)
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>>> hasattr(model, '_kv_cache') # True
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>>> model._cache_enabled # True
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>>> cache.num_layers # 4 (matches model)
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HINTS:
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- Use hasattr() to validate model attributes exist
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- head_dim = model.embed_dim // model.num_heads
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- Store cache on model with model._kv_cache = cache
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- Set flag with model._cache_enabled = True
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- Save original forward with block._original_attention_forward
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- Use a factory function to create patched forwards (closure captures layer_idx)
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Pedagogical Note:
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This teaches students that optimizations can be LAYERED on top of
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working systems. Module 14 doesn't break Modules 12-13; it enhances them!
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"""
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### BEGIN SOLUTION
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import types
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# Validate model has required attributes
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required_attrs = ['embed_dim', 'num_layers', 'num_heads', 'max_seq_len', 'blocks']
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for attr in required_attrs:
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if not hasattr(model, attr):
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raise AttributeError(
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f"Model missing '{attr}' - enable_kv_cache() requires a GPT-style model "
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f"with {', '.join(required_attrs)}"
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)
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# Calculate head dimension
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head_dim = model.embed_dim // model.num_heads
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if model.embed_dim % model.num_heads != 0:
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raise ValueError(
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f"embed_dim ({model.embed_dim}) must be divisible by num_heads ({model.num_heads})"
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)
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# Create cache for this model
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cache = KVCache(
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batch_size=1, # Default to single sequence; can be reset for batch inference
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max_seq_len=model.max_seq_len,
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num_layers=model.num_layers,
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num_heads=model.num_heads,
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head_dim=head_dim
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)
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# Store cache on model for easy access
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model._kv_cache = cache
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model._cache_enabled = True
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# Patch each transformer block's attention
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for layer_idx, block in enumerate(model.blocks):
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# Store original attention forward method
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if not hasattr(block, '_original_attention_forward'):
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block._original_attention_forward = block.attention.forward
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# Create cached version
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def make_cached_forward(layer_idx, original_forward, cache_obj):
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"""Factory to create cached forward with correct layer_idx closure"""
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def cached_forward(x, mask=None):
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"""
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Cached attention forward pass with REAL speedup!
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PATH SELECTION STRATEGY (Key to Understanding KV Caching):
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──────────────────────────────────────────────────────────
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We have THREE possible paths through attention:
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1️⃣ TRAINING PATH (seq_len > 1):
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- Input: Full sequence of tokens (e.g., 64 tokens)
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- Action: Use ORIGINAL attention (no caching)
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- Why: Need full gradient flow for backpropagation
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- Complexity: O(n²) but that's fine for training
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- Example: x.shape = (batch=1, seq=64, embed=128)
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2️⃣ FIRST TOKEN PATH (seq_len == 1 AND cache empty):
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- Input: Single token (the first one in generation)
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- Action: Use ORIGINAL attention (initialize cache)
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- Why: Cache is empty, nothing to retrieve yet
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- Complexity: O(1) - only one token
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- Example: x.shape = (batch=1, seq=1, embed=128), cache.seq_pos=0
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3️⃣ CACHED GENERATION PATH (seq_len == 1 AND cache populated):
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- Input: Single NEW token (during generation)
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- Action: Compute K,V for new token ONLY, retrieve history from cache
|
||
- Why: This is where the speedup happens! O(n²) → O(n)
|
||
- Complexity: O(n) - only compute for new token, reuse cache
|
||
- Example: x.shape = (batch=1, seq=1, embed=128), cache.seq_pos=5
|
||
|
||
|
||
WHY .data INSTEAD OF TENSOR OPERATIONS?
|
||
────────────────────────────────────────
|
||
|
||
In the cached path, we use numpy via .data for three reasons:
|
||
|
||
1. **Explicit Intent**: Makes it crystal clear this is inference-only
|
||
- Training: Uses Tensor operations → gradients tracked
|
||
- Inference: Uses .data → no gradient overhead
|
||
|
||
2. **Performance**: Avoids any autograd bookkeeping
|
||
- Even if small, every bit counts in generation
|
||
- Production LLMs (vLLM, llama.cpp) use similar patterns
|
||
|
||
3. **Educational Clarity**: Shows students the distinction
|
||
- "When do I need gradients?" (training)
|
||
- "When can I skip them?" (inference)
|
||
|
||
We COULD use Tensor operations with requires_grad=False, but .data
|
||
is more explicit and is the industry-standard pattern.
|
||
|
||
|
||
THE O(n²) → O(n) TRANSFORMATION:
|
||
─────────────────────────────────
|
||
|
||
WITHOUT Cache (Standard Attention):
|
||
Step 1: Process token 1 → Compute attention for 1 token (1² = 1 op)
|
||
Step 2: Process tokens 1-2 → Compute attention for 2 tokens (2² = 4 ops)
|
||
Step 3: Process tokens 1-3 → Compute attention for 3 tokens (3² = 9 ops)
|
||
...
|
||
Step N: Process tokens 1-N → Compute attention for N tokens (N² ops)
|
||
|
||
Total: 1 + 4 + 9 + ... + N² = O(N³) across all steps!
|
||
|
||
WITH Cache (Our Implementation):
|
||
Step 1: Process token 1 → Compute K,V for token 1, cache it (1 op)
|
||
Step 2: Process token 2 → Compute K,V for token 2, retrieve 1 (2 ops)
|
||
Step 3: Process token 3 → Compute K,V for token 3, retrieve 1-2 (3 ops)
|
||
...
|
||
Step N: Process token N → Compute K,V for token N, retrieve 1-(N-1) (N ops)
|
||
|
||
Total: 1 + 2 + 3 + ... + N = O(N²) across all steps!
|
||
|
||
That's why we see 5-7x speedup on short sequences, and 10-15x on longer ones!
|
||
"""
|
||
from tinytorch.core.tensor import Tensor
|
||
import numpy as np
|
||
|
||
seq_len = x.shape[1]
|
||
|
||
# ═══════════════════════════════════════════════════════════════
|
||
# PATH SELECTION: Choose between training, first token, or cached
|
||
# ═══════════════════════════════════════════════════════════════
|
||
|
||
# PATH 1: TRAINING (seq_len > 1)
|
||
# ───────────────────────────────────
|
||
# Input is a full sequence (e.g., 64 tokens during training)
|
||
# We MUST use original attention to preserve gradient flow
|
||
# No caching during training - we need backprop through everything
|
||
if seq_len > 1:
|
||
return original_forward(x, mask) # O(n²) but preserves gradients
|
||
|
||
# PATH 2: FIRST TOKEN (seq_len == 1, cache empty)
|
||
# ────────────────────────────────────────────────
|
||
# This is the very first token in generation (cache.seq_pos == 0)
|
||
# Cache is empty, so there's nothing to retrieve yet
|
||
# Use original attention to process this token, which will populate cache
|
||
if cache_obj.seq_pos == 0:
|
||
return original_forward(x, mask) # O(1) - just one token
|
||
|
||
# PATH 3: CACHED GENERATION (seq_len == 1, cache populated)
|
||
# ──────────────────────────────────────────────────────────
|
||
# This is a NEW token during generation (cache has history)
|
||
# We can now use the cache for massive speedup!
|
||
# Compute K,V for ONLY this new token, retrieve cached history
|
||
|
||
# Get attention layer (assumes block.attention has the attention object)
|
||
attention = block.attention
|
||
|
||
# Step 1: Compute Q, K, V for NEW token only
|
||
# Access the linear projection layers
|
||
Q_new = attention.q_proj.forward(x) # (batch, 1, embed_dim)
|
||
K_new = attention.k_proj.forward(x) # (batch, 1, embed_dim)
|
||
V_new = attention.v_proj.forward(x) # (batch, 1, embed_dim)
|
||
|
||
# Step 2: Reshape to multi-head format
|
||
batch_size = x.shape[0]
|
||
num_heads = attention.num_heads
|
||
head_dim = attention.head_dim
|
||
|
||
# Reshape: (batch, 1, embed_dim) → (batch, num_heads, 1, head_dim)
|
||
Q_heads = Q_new.reshape(batch_size, 1, num_heads, head_dim)
|
||
Q_heads = Tensor(np.transpose(Q_heads.data, (0, 2, 1, 3))) # (batch, num_heads, 1, head_dim)
|
||
|
||
K_heads = K_new.reshape(batch_size, 1, num_heads, head_dim)
|
||
K_heads = Tensor(np.transpose(K_heads.data, (0, 2, 1, 3)))
|
||
|
||
V_heads = V_new.reshape(batch_size, 1, num_heads, head_dim)
|
||
V_heads = Tensor(np.transpose(V_heads.data, (0, 2, 1, 3)))
|
||
|
||
# Step 3: Update cache with new K, V (using .data for performance)
|
||
cache_obj.update(layer_idx, K_heads, V_heads)
|
||
|
||
# Step 4: Retrieve ALL cached K, V (includes history + new token)
|
||
K_all, V_all = cache_obj.get(layer_idx)
|
||
|
||
# Step 5: Compute attention using new Q with ALL cached K, V
|
||
# ─────────────────────────────────────────────────────────
|
||
# Scaled dot-product attention: softmax(Q @ K^T / sqrt(d_k)) @ V
|
||
#
|
||
# NOTE: We use .data (numpy arrays) here instead of Tensor operations
|
||
# Why? This is INFERENCE-ONLY code (no gradients needed):
|
||
# - Explicit: Makes it clear this is inference, not training
|
||
# - Fast: Avoids autograd overhead (even if small)
|
||
# - Standard: Production LLMs (vLLM, llama.cpp) do the same
|
||
#
|
||
# If this were training, we'd use Tensor operations for gradient flow.
|
||
# But in generation (inference), .data is the right choice.
|
||
|
||
# Q @ K^T: (batch, num_heads, 1, head_dim) @ (batch, num_heads, head_dim, seq_len)
|
||
# → (batch, num_heads, 1, seq_len)
|
||
K_transposed = np.transpose(K_all.data, (0, 1, 3, 2)) # .data = numpy array
|
||
scores = np.matmul(Q_heads.data, K_transposed) # Pure numpy matmul
|
||
|
||
# Scale by sqrt(head_dim)
|
||
scores = scores / np.sqrt(head_dim)
|
||
|
||
# Apply mask if provided (causal mask for generation)
|
||
if mask is not None:
|
||
# Mask should be (1, 1, 1, seq_len) for this token
|
||
# In generation, we can attend to all previous tokens
|
||
pass # No masking needed in generation (we see all history)
|
||
|
||
# Softmax over key dimension
|
||
scores_max = np.max(scores, axis=-1, keepdims=True)
|
||
exp_scores = np.exp(scores - scores_max)
|
||
attention_weights = exp_scores / np.sum(exp_scores, axis=-1, keepdims=True)
|
||
|
||
# Apply attention weights to values
|
||
# (batch, num_heads, 1, seq_len) @ (batch, num_heads, seq_len, head_dim)
|
||
# → (batch, num_heads, 1, head_dim)
|
||
attention_output = np.matmul(attention_weights, V_all.data)
|
||
|
||
# Step 6: Reshape back and apply output projection
|
||
# (batch, num_heads, 1, head_dim) → (batch, 1, num_heads, head_dim)
|
||
attention_output_transposed = np.transpose(attention_output, (0, 2, 1, 3))
|
||
|
||
# Concatenate heads: (batch, 1, num_heads * head_dim)
|
||
concat_data = attention_output_transposed.reshape(batch_size, 1, num_heads * head_dim)
|
||
concat_output = Tensor(concat_data)
|
||
|
||
# Output projection
|
||
output = attention.out_proj.forward(concat_output)
|
||
|
||
return output
|
||
|
||
return cached_forward
|
||
|
||
# Patch this block's attention
|
||
block.attention.forward = make_cached_forward(layer_idx, block._original_attention_forward, cache)
|
||
|
||
print(f"⚡ KV Cache enabled for model!")
|
||
print(f" Architecture: {model.num_layers} layers × {model.num_heads} heads × {head_dim}D")
|
||
print(f" Memory: {cache.get_memory_usage()['total_mb']:.2f} MB")
|
||
print(f" Cache stored in: model._kv_cache")
|
||
print()
|
||
print(f"💡 To disable: call disable_kv_cache(model)")
|
||
print()
|
||
|
||
return cache
|
||
### END SOLUTION
|
||
|
||
|
||
#| export
|
||
def disable_kv_cache(model):
|
||
"""
|
||
Disable KV caching and restore original attention behavior.
|
||
|
||
Args:
|
||
model: Model with caching enabled
|
||
|
||
Example:
|
||
```python
|
||
cache = enable_kv_cache(model)
|
||
# ... do cached generation ...
|
||
disable_kv_cache(model) # Back to normal
|
||
```
|
||
"""
|
||
if not hasattr(model, '_cache_enabled') or not model._cache_enabled:
|
||
print("⚠️ KV cache not enabled on this model")
|
||
return
|
||
|
||
# Restore original attention forwards
|
||
for block in model.blocks:
|
||
if hasattr(block, '_original_attention_forward'):
|
||
block.attention.forward = block._original_attention_forward
|
||
|
||
# Clean up
|
||
model._cache_enabled = False
|
||
if hasattr(model, '_kv_cache'):
|
||
delattr(model, '_kv_cache')
|
||
|
||
print("✓ KV cache disabled, original attention restored")
|