Add GPT-2 KV cache to pkg (#687)

ch04/03_kv-cache/gpt_with_kv_cache_optimized.py

@@ -80,8 +80,6 @@ class MultiHeadAttention(nn.Module):
             keys, values = keys_new, values_new
             self.ptr_cur = 0  # keep pointer sane if you interleave modes
         ####################################################
-
-
         # Compute scaled dot-product attention (aka self-attention) with a causal mask
         attn_scores = queries @ keys.transpose(2, 3)  # Dot product for each head
 

pkg/llms_from_scratch/README.md

@@ -113,7 +113,22 @@ from llms_from_scratch.appendix_d import find_highest_gradient, train_model
 ```
 
 
+
  
+
+### GPT-2 KV cache variant (Bonus material)
+
+```python
+from llms_from_scratch.kv_cache.gpt2 import GPTModel
+from llms_from_scratch.kv_cache.generate import generate_text_simple
+```
+
+For more information about KV caching, please see the [KV cache README](../../ch04/03_kv-cache).
+
+
+
+ 
+
 ### Llama  3 (Bonus material)
 
 ```python

pkg/llms_from_scratch/kv_cache/gpt2.py

@@ -0,0 +1,287 @@
+# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
+# Source for "Build a Large Language Model From Scratch"
+#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
+# Code: https://github.com/rasbt/LLMs-from-scratch
+
+import torch
+import torch.nn as nn
+
+
+#####################################
+# Chapter 3
+#####################################
+class MultiHeadAttention(nn.Module):
+    def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False, max_seq_len=None, window_size=None):
+        super().__init__()
+        assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
+
+        self.d_out = d_out
+        self.num_heads = num_heads
+        self.head_dim = d_out // num_heads  # Reduce the projection dim to match desired output dim
+
+        self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
+        self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
+        self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)
+        self.out_proj = nn.Linear(d_out, d_out)  # Linear layer to combine head outputs
+        self.dropout = nn.Dropout(dropout)
+
+        ####################################################
+        # NEW
+        self.max_seq_len = max_seq_len or context_length
+        self.window_size = window_size or self.max_seq_len
+        self.register_buffer("cache_k", None, persistent=False)
+        self.register_buffer("cache_v", None, persistent=False)
+        ####################################################
+
+    def forward(self, x, use_cache=False):
+        b, num_tokens, d_in = x.shape
+
+        keys_new = self.W_key(x)  # Shape: (b, num_tokens, d_out)
+        values_new = self.W_value(x)
+        queries = self.W_query(x)
+
+        # We implicitly split the matrix by adding a `num_heads` dimension
+        # Unroll last dim: (b, num_tokens, d_out) -> (b, num_tokens, num_heads, head_dim)
+        keys_new = keys_new.view(b, num_tokens, self.num_heads, self.head_dim)
+        values_new = values_new.view(b, num_tokens, self.num_heads, self.head_dim)
+        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
+
+        # Transpose: (b, num_tokens, num_heads, head_dim) -> (b, num_heads, num_tokens, head_dim)
+        keys_new = keys_new.transpose(1, 2)
+        values_new = values_new.transpose(1, 2)
+        queries = queries.transpose(1, 2)
+
+        ####################################################
+        # NEW
+        if use_cache:
+            if self.cache_k is None or self.cache_k.size(0) != b:
+                self.cache_k = torch.zeros(b, self.num_heads,
+                                           self.window_size, self.head_dim,
+                                           device=x.device)
+                self.cache_v = torch.zeros_like(self.cache_k)
+                self.ptr_cur = 0  # pointer to next free slot
+
+            # if incoming chunk would overflow discard oldest tokens
+            if self.ptr_cur + num_tokens > self.window_size:
+                overflow = self.ptr_cur + num_tokens - self.window_size
+                # shift everything left by `overflow` (cheap view-copy)
+                self.cache_k[:, :, :-overflow, :] = self.cache_k[:, :, overflow:, :].clone()
+                self.cache_v[:, :, :-overflow, :] = self.cache_v[:, :, overflow:, :].clone()
+                self.ptr_cur -= overflow  # pointer after shift
+
+            self.cache_k[:, :, self.ptr_cur:self.ptr_cur + num_tokens, :] = keys_new
+            self.cache_v[:, :, self.ptr_cur:self.ptr_cur + num_tokens, :] = values_new
+            self.ptr_cur += num_tokens
+
+            keys = self.cache_k[:, :, :self.ptr_cur, :]
+            values = self.cache_v[:, :, :self.ptr_cur, :]
+        else:
+            keys, values = keys_new, values_new
+            self.ptr_cur = 0  # keep pointer sane if you interleave modes
+        ####################################################
+        # Compute scaled dot-product attention (aka self-attention) with a causal mask
+        attn_scores = queries @ keys.transpose(2, 3)  # Dot product for each head
+
+        ####################################################
+        # NEW
+        K = attn_scores.size(-1)
+
+        if num_tokens == K:
+            # No cache → use the pre‑baked triangular mask slice
+            causal_mask = torch.triu(torch.ones(num_tokens, K, device=x.device, dtype=torch.bool), diagonal=1)
+        else:
+            # Cached: need to offset the diagonal by (K − num_tokens)
+            offset = K - num_tokens  # number of tokens already in cache before this chunk
+            row_idx = torch.arange(num_tokens, device=x.device).unsqueeze(1)  # (num_tokens, 1)
+            col_idx = torch.arange(K, device=x.device).unsqueeze(0)           # (1, K)
+            causal_mask = row_idx + offset < col_idx                          # True where j > i+offset
+        ####################################################
+
+        # Use the mask to fill attention scores
+        attn_scores.masked_fill_(causal_mask.unsqueeze(0).unsqueeze(0), -torch.inf)
+
+        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
+        attn_weights = self.dropout(attn_weights)
+
+        # Shape: (b, num_tokens, num_heads, head_dim)
+        context_vec = (attn_weights @ values).transpose(1, 2)
+
+        # Combine heads, where self.d_out = self.num_heads * self.head_dim
+        context_vec = context_vec.contiguous().view(b, num_tokens, self.d_out)
+        context_vec = self.out_proj(context_vec)  # optional projection
+
+        return context_vec
+
+    ####################################################
+    # NEW
+    def reset_cache(self):
+        self.cache_k, self.cache_v = None, None
+    ####################################################
+
+
+#####################################
+# Chapter 4
+#####################################
+class LayerNorm(nn.Module):
+    def __init__(self, emb_dim):
+        super().__init__()
+        self.eps = 1e-5
+        self.scale = nn.Parameter(torch.ones(emb_dim))
+        self.shift = nn.Parameter(torch.zeros(emb_dim))
+
+    def forward(self, x):
+        mean = x.mean(dim=-1, keepdim=True)
+        var = x.var(dim=-1, keepdim=True, unbiased=False)
+        norm_x = (x - mean) / torch.sqrt(var + self.eps)
+        return self.scale * norm_x + self.shift
+
+
+class GELU(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return 0.5 * x * (1 + torch.tanh(
+            torch.sqrt(torch.tensor(2.0 / torch.pi)) *
+            (x + 0.044715 * torch.pow(x, 3))
+        ))
+
+
+class FeedForward(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(cfg["emb_dim"], 4 * cfg["emb_dim"]),
+            GELU(),
+            nn.Linear(4 * cfg["emb_dim"], cfg["emb_dim"]),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.att = MultiHeadAttention(
+            d_in=cfg["emb_dim"],
+            d_out=cfg["emb_dim"],
+            context_length=cfg["context_length"],
+            num_heads=cfg["n_heads"],
+            dropout=cfg["drop_rate"],
+            qkv_bias=cfg["qkv_bias"],
+            window_size=cfg["kv_window_size"] if "kv_window_size" in cfg else cfg["context_length"]  # NEW
+        )
+        self.ff = FeedForward(cfg)
+        self.norm1 = LayerNorm(cfg["emb_dim"])
+        self.norm2 = LayerNorm(cfg["emb_dim"])
+        self.drop_shortcut = nn.Dropout(cfg["drop_rate"])
+
+    def forward(self, x, use_cache=False):
+        # Shortcut connection for attention block
+        shortcut = x
+        x = self.norm1(x)
+
+        # x = self.att(x)   # Shape [batch_size, num_tokens, emb_size]
+        ####################################################
+        # NEW
+        x = self.att(x, use_cache=use_cache)
+        ####################################################
+
+        x = self.drop_shortcut(x)
+        x = x + shortcut  # Add the original input back
+
+        # Shortcut connection for feed-forward block
+        shortcut = x
+        x = self.norm2(x)
+        x = self.ff(x)
+        x = self.drop_shortcut(x)
+        x = x + shortcut  # Add the original input back
+
+        return x
+
+
+class GPTModel(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"])
+        self.pos_emb = nn.Embedding(cfg["context_length"], cfg["emb_dim"])
+        self.drop_emb = nn.Dropout(cfg["drop_rate"])
+
+        # self.trf_blocks = nn.Sequential(
+        #    *[TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
+        ####################################################
+        # NEW
+        self.trf_blocks = nn.ModuleList(
+            [TransformerBlock(cfg) for _ in range(cfg["n_layers"])])
+
+        self.ptr_current_pos = 0
+        ####################################################
+
+        self.final_norm = LayerNorm(cfg["emb_dim"])
+        self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False)
+
+    def forward(self, in_idx, use_cache=False):
+        batch_size, seq_len = in_idx.shape
+        tok_embeds = self.tok_emb(in_idx)
+
+        # pos_embeds = self.pos_emb(torch.arange(seq_len, device=in_idx.device))
+
+        ####################################################
+        # NEW
+
+        if use_cache:
+            pos_ids = torch.arange(self.ptr_current_pos, self.ptr_current_pos + seq_len, device=in_idx.device, dtype=torch.long)
+            self.ptr_current_pos += seq_len
+        else:
+            pos_ids = torch.arange(0, seq_len, device=in_idx.device, dtype=torch.long)
+        pos_embeds = self.pos_emb(pos_ids).unsqueeze(0)
+        ####################################################
+
+        x = tok_embeds + pos_embeds  # Shape [batch_size, num_tokens, emb_size]
+        x = self.drop_emb(x)
+
+        # x = self.trf_blocks(x)
+        ####################################################
+        # NEW
+        for blk in self.trf_blocks:
+            x = blk(x, use_cache=use_cache)
+        ####################################################
+
+        x = self.final_norm(x)
+        logits = self.out_head(x)
+        return logits
+
+    ####################################################
+    # NEW
+    def reset_kv_cache(self):
+        for blk in self.trf_blocks:
+            blk.att.reset_cache()
+        self.ptr_current_pos = 0
+    ####################################################
+
+
+def generate_text_simple(model, idx, max_new_tokens, context_size):
+    # idx is (B, T) array of indices in the current context
+    for _ in range(max_new_tokens):
+
+        # Crop current context if it exceeds the supported context size
+        # E.g., if LLM supports only 5 tokens, and the context size is 10
+        # then only the last 5 tokens are used as context
+        idx_cond = idx[:, -context_size:]
+
+        # Get the predictions
+        with torch.no_grad():
+            logits = model(idx_cond)
+
+        # Focus only on the last time step
+        # (batch, n_token, vocab_size) becomes (batch, vocab_size)
+        logits = logits[:, -1, :]
+
+        # Get the idx of the vocab entry with the highest logits value
+        idx_next = torch.argmax(logits, dim=-1, keepdim=True)  # (batch, 1)
+
+        # Append sampled index to the running sequence
+        idx = torch.cat((idx, idx_next), dim=1)  # (batch, n_tokens+1)
+
+    return idx

pkg/llms_from_scratch/tests/test_ch04.py

@@ -4,7 +4,9 @@
 # Code: https://github.com/rasbt/LLMs-from-scratch
 
 from llms_from_scratch.ch04 import GPTModel, GPTModelFast
+from llms_from_scratch.kv_cache.gpt2 import GPTModel as GPTModelKV
 from llms_from_scratch.ch04 import generate_text_simple
+from llms_from_scratch.kv_cache.generate import generate_text_simple as generate_text_simple_cached
 
 import pytest
 import torch
@@ -22,8 +24,16 @@ GPT_CONFIG_124M = {
 }
 
 
-@pytest.mark.parametrize("ModelClass", [GPTModel, GPTModelFast])
-def test_gpt_model_variants(ModelClass):
+@pytest.mark.parametrize("ModelClass", [GPTModel, GPTModelFast, GPTModelKV])
+@pytest.mark.parametrize("generate_fn", [generate_text_simple, generate_text_simple_cached])
+def test_gpt_model_variants(ModelClass, generate_fn):
+
+    # Skip incompatible combinations
+    if generate_fn is generate_text_simple and getattr(ModelClass, "reset_kv_cache", False):
+        return
+    if generate_fn is generate_text_simple_cached and not getattr(ModelClass, "reset_kv_cache", False):
+        return
+
     torch.manual_seed(123)
     model = ModelClass(GPT_CONFIG_124M)
     model.eval()  # disable dropout
@@ -39,7 +49,7 @@ def test_gpt_model_variants(ModelClass):
     print("Encoded input text:", encoded)
     print("encoded_tensor.shape:", encoded_tensor.shape)
 
-    out = generate_text_simple(
+    out = generate_fn(
         model=model,
         idx=encoded_tensor,
         max_new_tokens=10,