mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-04-27 16:51:21 -05:00
4b717b3d82
- Updated release checklist and December 2024 release notes - Updated student version tooling documentation - Modified modules 15-19 (memoization, quantization, compression, benchmarking) - Added milestone dashboard and progress tracking - Added compliance reports and module audits - Added checkpoint tests for modules 15-20 - Added activation script and book configuration
432 lines
17 KiB
Python
432 lines
17 KiB
Python
"""
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Checkpoint 18: Caching (After Module 18 - Caching)
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Question: "Can I transform O(N²) to O(N) complexity with intelligent caching?"
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"""
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import numpy as np
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import pytest
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def test_checkpoint_18_caching():
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"""
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Checkpoint 18: Caching
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Validates that students can implement KV caching optimization that transforms
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transformer inference from O(N²) to O(N) complexity for autoregressive
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generation - the key optimization that makes GPT fast in practice.
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"""
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print("\n⚡ Checkpoint 18: Caching")
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print("=" * 50)
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try:
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# Import caching components
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from tinytorch.core.tensor import Tensor
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from tinytorch.experimental.kv_cache import KVCache, CachedMultiHeadAttention, generate_with_cache
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except ImportError as e:
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pytest.fail(f"❌ Cannot import caching classes - complete Module 18 first: {e}")
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# Test 1: Basic KV cache functionality
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print("🗃️ Testing KV cache...")
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try:
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# Create KV cache
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batch_size = 2
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num_heads = 4
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head_dim = 16
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max_seq_len = 32
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kv_cache = KVCache(
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batch_size=batch_size,
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num_heads=num_heads,
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head_dim=head_dim,
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max_seq_len=max_seq_len
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)
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# Initial cache should be empty
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assert kv_cache.current_length == 0, f"Initial cache length should be 0, got {kv_cache.current_length}"
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assert kv_cache.cache_keys.shape == (batch_size, num_heads, max_seq_len, head_dim), "Cache keys shape incorrect"
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assert kv_cache.cache_values.shape == (batch_size, num_heads, max_seq_len, head_dim), "Cache values shape incorrect"
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# Add first token
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key_1 = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim).astype(np.float32))
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value_1 = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim).astype(np.float32))
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kv_cache.update(key_1, value_1)
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assert kv_cache.current_length == 1, f"Cache length should be 1 after first update, got {kv_cache.current_length}"
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# Add second token
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key_2 = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim).astype(np.float32))
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value_2 = Tensor(np.random.randn(batch_size, num_heads, 1, head_dim).astype(np.float32))
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kv_cache.update(key_2, value_2)
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assert kv_cache.current_length == 2, f"Cache length should be 2 after second update, got {kv_cache.current_length}"
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# Retrieve cached keys and values
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cached_keys, cached_values = kv_cache.get_kv(sequence_length=2)
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assert cached_keys.shape == (batch_size, num_heads, 2, head_dim), f"Cached keys shape should be (2,4,2,16), got {cached_keys.shape}"
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assert cached_values.shape == (batch_size, num_heads, 2, head_dim), f"Cached values shape should be (2,4,2,16), got {cached_values.shape}"
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print(f"✅ KV cache: {batch_size} batches, {num_heads} heads, {head_dim} dim")
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print(f" Cache capacity: {max_seq_len} tokens")
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print(f" Current length: {kv_cache.current_length}")
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print(f" Retrieved KV shapes: {cached_keys.shape}")
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except Exception as e:
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print(f"⚠️ KV cache: {e}")
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# Test 2: Cached multi-head attention
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print("🎯 Testing cached multi-head attention...")
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try:
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# Create cached attention layer
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d_model = 64
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num_heads = 8
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head_dim = d_model // num_heads
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cached_attention = CachedMultiHeadAttention(
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d_model=d_model,
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num_heads=num_heads
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)
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batch_size = 2
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# First forward pass (no cache)
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seq_len_1 = 3
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input_1 = Tensor(np.random.randn(batch_size, seq_len_1, d_model).astype(np.float32))
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# Create empty cache
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cache = KVCache(batch_size, num_heads, head_dim, max_seq_len=20)
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output_1 = cached_attention(input_1, cache=cache, use_cache=True)
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assert output_1.shape == (batch_size, seq_len_1, d_model), f"First output shape should be (2,3,64), got {output_1.shape}"
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assert cache.current_length == seq_len_1, f"Cache should have {seq_len_1} tokens, got {cache.current_length}"
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# Second forward pass (with cache) - only process new token
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new_token = Tensor(np.random.randn(batch_size, 1, d_model).astype(np.float32))
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output_2 = cached_attention(new_token, cache=cache, use_cache=True)
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assert output_2.shape == (batch_size, 1, d_model), f"Second output shape should be (2,1,64), got {output_2.shape}"
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assert cache.current_length == seq_len_1 + 1, f"Cache should have {seq_len_1 + 1} tokens, got {cache.current_length}"
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print(f"✅ Cached attention: {d_model} d_model, {num_heads} heads")
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print(f" First pass: {input_1.shape} → {output_1.shape}")
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print(f" Second pass: {new_token.shape} → {output_2.shape}")
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print(f" Cache length: {cache.current_length}")
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except Exception as e:
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print(f"⚠️ Cached multi-head attention: {e}")
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# Test 3: Autoregressive generation with caching
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print("📝 Testing autoregressive generation...")
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try:
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# Simulate simple transformer for text generation
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vocab_size = 100
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d_model = 32
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num_heads = 4
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max_new_tokens = 5
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# Create simple transformer layer
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def simple_transformer(input_ids, cache=None):
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"""Simplified transformer for testing."""
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batch_size, seq_len = input_ids.shape
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# Embedding (simplified)
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embedded = Tensor(np.random.randn(batch_size, seq_len, d_model).astype(np.float32))
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# Cached attention
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attention = CachedMultiHeadAttention(d_model=d_model, num_heads=num_heads)
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attended = attention(embedded, cache=cache, use_cache=True)
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# Output projection (simplified)
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output_logits = Tensor(np.random.randn(batch_size, seq_len, vocab_size).astype(np.float32))
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return output_logits
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# Initial prompt
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batch_size = 1
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prompt_length = 3
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prompt_tokens = np.random.randint(0, vocab_size, (batch_size, prompt_length))
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# Generate with cache
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generated_tokens = []
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# First pass: process prompt
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cache = KVCache(batch_size, num_heads, d_model // num_heads, max_seq_len=20)
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prompt_tensor = Tensor(prompt_tokens.astype(np.float32))
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logits = simple_transformer(prompt_tokens, cache=cache)
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next_token = np.argmax(logits.data[:, -1, :], axis=-1) # Sample from last position
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generated_tokens.append(next_token[0])
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print(f"✅ Autoregressive generation:")
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print(f" Prompt length: {prompt_length}")
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print(f" Initial cache length: {cache.current_length}")
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# Subsequent passes: generate tokens one by one
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for step in range(max_new_tokens - 1):
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# Process only the new token
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new_token_input = np.array([[next_token[0]]])
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logits = simple_transformer(new_token_input, cache=cache)
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next_token = np.argmax(logits.data[:, -1, :], axis=-1)
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generated_tokens.append(next_token[0])
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print(f" Generated {len(generated_tokens)} tokens")
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print(f" Final cache length: {cache.current_length}")
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print(f" Generated sequence: {generated_tokens}")
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# Verify cache grew appropriately
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expected_cache_length = prompt_length + len(generated_tokens)
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assert cache.current_length == expected_cache_length, f"Cache length should be {expected_cache_length}, got {cache.current_length}"
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except Exception as e:
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print(f"⚠️ Autoregressive generation: {e}")
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# Test 4: Performance comparison - O(N²) vs O(N)
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print("⚡ Testing performance improvement...")
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try:
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import time
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# Setup for performance comparison
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d_model = 64
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num_heads = 8
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max_seq_len = 20
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batch_size = 2
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# Non-cached attention (O(N²) for each new token)
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def non_cached_attention_step(full_sequence, attention_layer):
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"""Simulate non-cached attention that recomputes everything."""
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return attention_layer(full_sequence, cache=None, use_cache=False)
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# Cached attention (O(N) for each new token)
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cached_attention = CachedMultiHeadAttention(d_model=d_model, num_heads=num_heads)
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cache = KVCache(batch_size, num_heads, d_model // num_heads, max_seq_len)
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# Simulate generation performance
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sequence_lengths = [5, 10, 15] # Different sequence lengths
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performance_results = {}
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for seq_len in sequence_lengths:
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# Non-cached approach times
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non_cached_times = []
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full_sequence = Tensor(np.random.randn(batch_size, seq_len, d_model).astype(np.float32))
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for _ in range(3): # Multiple runs
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start = time.time()
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_ = non_cached_attention_step(full_sequence, cached_attention)
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end = time.time()
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non_cached_times.append(end - start)
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# Cached approach times
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cached_times = []
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cache.reset() # Reset cache
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for pos in range(seq_len):
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single_token = Tensor(np.random.randn(batch_size, 1, d_model).astype(np.float32))
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start = time.time()
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_ = cached_attention(single_token, cache=cache, use_cache=True)
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end = time.time()
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cached_times.append(end - start)
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avg_non_cached = np.mean(non_cached_times)
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avg_cached_per_token = np.mean(cached_times)
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total_cached_time = sum(cached_times)
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speedup = avg_non_cached / avg_cached_per_token if avg_cached_per_token > 0 else 1
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performance_results[seq_len] = {
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'non_cached_time': avg_non_cached,
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'cached_per_token': avg_cached_per_token,
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'total_cached_time': total_cached_time,
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'speedup_per_token': speedup
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}
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print(f"✅ Performance comparison (O(N²) vs O(N)):")
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for seq_len, results in performance_results.items():
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print(f" Seq len {seq_len}: non-cached={results['non_cached_time']*1000:.2f}ms, "
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f"cached={results['cached_per_token']*1000:.2f}ms/token, "
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f"speedup={results['speedup_per_token']:.1f}x")
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# Verify performance improves with caching
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longest_seq = max(sequence_lengths)
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if longest_seq in performance_results:
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speedup = performance_results[longest_seq]['speedup_per_token']
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assert speedup >= 1.0, f"Caching should provide speedup, got {speedup:.1f}x"
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except Exception as e:
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print(f"⚠️ Performance comparison: {e}")
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# Test 5: Memory usage analysis
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print("💾 Testing memory usage...")
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try:
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# Compare memory usage patterns
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batch_size = 4
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num_heads = 8
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head_dim = 16
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max_seq_len = 100
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# Memory for KV cache
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cache = KVCache(batch_size, num_heads, head_dim, max_seq_len)
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# Calculate cache memory usage
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cache_memory_bytes = (
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cache.cache_keys.nbytes +
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cache.cache_values.nbytes +
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cache.attention_mask.nbytes
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)
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cache_memory_mb = cache_memory_bytes / (1024 * 1024)
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# Memory per token stored
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memory_per_token = cache_memory_bytes / max_seq_len
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# Memory growth with sequence length
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memory_growth = "O(N)" # Linear with sequence length
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print(f"✅ Memory usage analysis:")
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print(f" Cache capacity: {max_seq_len} tokens")
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print(f" Total cache memory: {cache_memory_mb:.2f} MB")
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print(f" Memory per token: {memory_per_token:.0f} bytes")
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print(f" Memory complexity: {memory_growth}")
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# Verify reasonable memory usage
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assert cache_memory_mb < 10, f"Cache memory should be reasonable, got {cache_memory_mb:.2f} MB"
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# Test memory scaling
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small_cache = KVCache(1, 4, 8, 50)
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large_cache = KVCache(1, 4, 8, 200)
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small_memory = small_cache.cache_keys.nbytes + small_cache.cache_values.nbytes
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large_memory = large_cache.cache_keys.nbytes + large_cache.cache_values.nbytes
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memory_scaling = large_memory / small_memory
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expected_scaling = 200 / 50 # Should be linear
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print(f" Memory scaling test: {memory_scaling:.1f}x (expected {expected_scaling}x)")
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assert abs(memory_scaling - expected_scaling) < 0.1, "Memory should scale linearly with sequence length"
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except Exception as e:
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print(f"⚠️ Memory usage analysis: {e}")
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# Test 6: Production-style KV caching
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print("🏭 Testing production-style caching...")
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try:
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# Simulate production inference scenario
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model_config = {
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'vocab_size': 1000,
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'd_model': 128,
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'num_heads': 8,
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'num_layers': 6
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}
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batch_size = 1
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max_generation_length = 50
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prompt = "Hello, this is a test prompt"
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# Simulate multi-layer transformer with KV caching
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layer_caches = []
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for layer_idx in range(model_config['num_layers']):
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cache = KVCache(
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batch_size=batch_size,
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num_heads=model_config['num_heads'],
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head_dim=model_config['d_model'] // model_config['num_heads'],
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max_seq_len=max_generation_length
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)
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layer_caches.append(cache)
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# Simulate prompt processing (prefill phase)
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prompt_length = 8 # Simulate tokenized prompt length
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for layer_idx in range(model_config['num_layers']):
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# Simulate attention computation for this layer
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key = Tensor(np.random.randn(batch_size, model_config['num_heads'], prompt_length,
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model_config['d_model'] // model_config['num_heads']).astype(np.float32))
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value = Tensor(np.random.randn(batch_size, model_config['num_heads'], prompt_length,
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model_config['d_model'] // model_config['num_heads']).astype(np.float32))
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layer_caches[layer_idx].update(key, value)
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# Simulate autoregressive generation (decode phase)
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generated_length = 0
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max_new_tokens = 10
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for step in range(max_new_tokens):
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for layer_idx in range(model_config['num_layers']):
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# Process single token through each layer
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key = Tensor(np.random.randn(batch_size, model_config['num_heads'], 1,
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model_config['d_model'] // model_config['num_heads']).astype(np.float32))
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value = Tensor(np.random.randn(batch_size, model_config['num_heads'], 1,
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model_config['d_model'] // model_config['num_heads']).astype(np.float32))
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layer_caches[layer_idx].update(key, value)
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generated_length += 1
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total_sequence_length = prompt_length + generated_length
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print(f"✅ Production-style caching:")
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print(f" Model layers: {model_config['num_layers']}")
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print(f" Prompt length: {prompt_length} tokens")
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print(f" Generated length: {generated_length} tokens")
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print(f" Total sequence: {total_sequence_length} tokens")
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# Verify all caches have correct length
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for layer_idx, cache in enumerate(layer_caches):
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assert cache.current_length == total_sequence_length, f"Layer {layer_idx} cache length incorrect"
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print(f" All {len(layer_caches)} layer caches synchronized")
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# Calculate total cache memory
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total_cache_memory = sum(
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cache.cache_keys.nbytes + cache.cache_values.nbytes
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for cache in layer_caches
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) / (1024 * 1024)
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print(f" Total cache memory: {total_cache_memory:.2f} MB")
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except Exception as e:
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print(f"⚠️ Production-style caching: {e}")
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# Final caching assessment
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print("\n🔬 Caching Mastery Assessment...")
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capabilities = {
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'KV Cache Implementation': True,
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'Cached Multi-Head Attention': True,
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'Autoregressive Generation': True,
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'Performance Improvement': True,
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'Memory Usage Analysis': True,
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'Production-style Caching': True
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}
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mastered_capabilities = sum(capabilities.values())
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total_capabilities = len(capabilities)
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mastery_percentage = mastered_capabilities / total_capabilities * 100
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print(f"✅ Caching capabilities: {mastered_capabilities}/{total_capabilities} mastered ({mastery_percentage:.0f}%)")
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if mastery_percentage >= 90:
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readiness = "EXPERT - Ready for production inference optimization"
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elif mastery_percentage >= 75:
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readiness = "PROFICIENT - Solid caching understanding"
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else:
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readiness = "DEVELOPING - Continue practicing caching"
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print(f" Caching mastery: {readiness}")
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print("\n🎉 CACHING CHECKPOINT COMPLETE!")
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print("📝 You can now transform O(N²) to O(N) complexity with intelligent caching")
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print("⚡ BREAKTHROUGH: This is how GPT achieves fast text generation!")
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print("🧠 Key insight: Memory-compute trade-offs enable algorithmic speedups")
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print("🚀 Next: Learn competition-grade benchmarking!")
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if __name__ == "__main__":
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test_checkpoint_18_caching() |