Add Llama 3.2 to pkg (#591)

* Add Llama 3.2 to pkg

* remove redundant attributes

* update tests

* updates

* updates

* updates

* fix link

* fix link

.github/workflows/basic-tests-linux-uv.yml

@@ -71,4 +71,5 @@ jobs:
         shell: bash
         run: |
           source .venv/bin/activate
+          uv pip install transformers
           pytest pkg/llms_from_scratch/tests/

.github/workflows/check-links.yml

@@ -24,8 +24,6 @@ jobs:
       run: |
         curl -LsSf https://astral.sh/uv/install.sh | sh
         uv add pytest-ruff pytest-check-links
-        # Current version of retry doesn't work well if there are broken non-URL links
-        # pip install pytest pytest-check-links pytest-retry
 
     - name: Check links
       run: |
@@ -40,5 +38,3 @@ jobs:
           --check-links-ignore "https://arxiv.org/*" \
           --check-links-ignore "https://ai.stanford.edu/~amaas/data/sentiment/" \
           --check-links-ignore "https://x.com/*"
-        # pytest --check-links ./ --check-links-ignore "https://platform.openai.com/*" --check-links-ignore "https://arena.lmsys.org" --retries 2 --retry-delay 5
-

ch05/07_gpt_to_llama/README.md

@@ -8,4 +8,188 @@ This folder contains code for converting the GPT implementation from chapter 4 a
 - [converting-llama2-to-llama3.ipynb](converting-llama2-to-llama3.ipynb): contains code to convert the Llama 2 model to Llama 3, Llama 3.1, and Llama 3.2
 - [standalone-llama32.ipynb](standalone-llama32.ipynb): a standalone notebook implementing Llama 3.2
 
-<img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/gpt-and-all-llamas.webp">
+<img src="https://sebastianraschka.com/images/LLMs-from-scratch-images/bonus/gpt-to-llama/gpt-and-all-llamas.webp">
+
+
+&nbsp;
+### Using Llama 3.2 via the `llms-from-scratch` package
+
+For an easy way to use the Llama 3.2 1B and 3B models, you can also use the `llms-from-scratch` PyPI package based on the source code in this repository at [pkg/llms_from_scratch](../../pkg/llms_from_scratch).
+
+&nbsp;
+##### 1) Installation
+
+```bash
+pip install llms_from_scratch blobfile
+```
+&nbsp;
+##### 2) Model and text generation settings
+
+Specify which model to use:
+
+```python
+MODEL_FILE = "llama3.2-1B-instruct.pth"
+# MODEL_FILE = "llama3.2-1B-base.pth"
+# MODEL_FILE = "llama3.2-3B-instruct.pth"
+# MODEL_FILE = "llama3.2-3B-base.pth"
+```
+
+Basic text generation settings that can be defined by the user. Note that the recommended 8192-token context size requires approximately 3 GB of VRAM for the text generation example.
+
+```python
+MODEL_CONTEXT_LENGTH = 8192  # Supports up to 131_072
+
+# Text generation settings
+if "instruct" in MODEL_FILE:
+    PROMPT = "What do llamas eat?"
+else:
+    PROMPT = "Llamas eat"
+
+MAX_NEW_TOKENS = 150
+TEMPERATURE = 0.
+TOP_K = 1
+```
+
+&nbsp;
+##### 3) Weight download and loading
+
+This automatically downloads the weight file based on the model choice above:
+
+```python
+import os
+import urllib.request
+
+url = f"https://huggingface.co/rasbt/llama-3.2-from-scratch/resolve/main/{MODEL_FILE}"
+
+if not os.path.exists(MODEL_FILE):
+    urllib.request.urlretrieve(url, MODEL_FILE)
+    print(f"Downloaded to {MODEL_FILE}")
+```
+
+The model weights are then loaded as follows:
+
+```python
+import torch
+from llms_from_scratch.llama3 import Llama3Model
+
+if "1B" in MODEL_FILE:
+    from llms_from_scratch.llama3 import LLAMA32_CONFIG_1B as LLAMA32_CONFIG
+elif "3B" in MODEL_FILE:
+    from llms_from_scratch.llama3 import LLAMA32_CONFIG_3B as LLAMA32_CONFIG
+else:
+    raise ValueError("Incorrect model file name")
+
+LLAMA32_CONFIG["context_length"] = MODEL_CONTEXT_LENGTH
+
+model = Llama3Model(LLAMA32_CONFIG)
+model.load_state_dict(torch.load(MODEL_FILE, weights_only=True))
+
+device = (
+    torch.device("cuda") if torch.cuda.is_available() else
+    torch.device("mps") if torch.backends.mps.is_available() else
+    torch.device("cpu")
+)
+model.to(device)
+```
+
+&nbsp;
+##### 4) Initialize tokenizer
+
+The following code downloads and initializes the tokenizer:
+
+```python
+from llms_from_scratch.llama3 import Llama3Tokenizer, ChatFormat, clean_text
+
+TOKENIZER_FILE = "tokenizer.model"
+
+url = f"https://huggingface.co/rasbt/llama-3.2-from-scratch/resolve/main/{TOKENIZER_FILE}"
+
+if not os.path.exists(TOKENIZER_FILE):
+    urllib.request.urlretrieve(url, TOKENIZER_FILE)
+    print(f"Downloaded to {TOKENIZER_FILE}")
+    
+tokenizer = Llama3Tokenizer("tokenizer.model")
+
+if "instruct" in MODEL_FILE:
+    tokenizer = ChatFormat(tokenizer)
+```
+
+&nbsp;
+##### 5) Generating text
+
+Lastly, we can generate text via the following code:
+
+```python
+import time
+
+from llms_from_scratch.ch05 import (
+    generate,
+    text_to_token_ids,
+    token_ids_to_text
+)
+
+torch.manual_seed(123)
+
+start = time.time()
+
+token_ids = generate(
+    model=model,
+    idx=text_to_token_ids(PROMPT, tokenizer).to(device),
+    max_new_tokens=MAX_NEW_TOKENS,
+    context_size=LLAMA32_CONFIG["context_length"],
+    top_k=TOP_K,
+    temperature=TEMPERATURE
+)
+
+print(f"Time: {time.time() - start:.2f} sec")
+
+if torch.cuda.is_available():
+    max_mem_bytes = torch.cuda.max_memory_allocated()
+    max_mem_gb = max_mem_bytes / (1024 ** 3)
+    print(f"Max memory allocated: {max_mem_gb:.2f} GB")
+
+output_text = token_ids_to_text(token_ids, tokenizer)
+
+if "instruct" in MODEL_FILE:
+    output_text = clean_text(output_text)
+
+print("\n\nOutput text:\n\n", output_text)
+```
+
+When using the Llama 3.2 1B Instruct model, the output should look similar to the one shown below:
+
+```
+Time: 4.12 sec
+Max memory allocated: 2.91 GB
+
+
+Output text:
+
+ Llamas are herbivores, which means they primarily eat plants. Their diet consists mainly of:
+
+1. Grasses: Llamas love to graze on various types of grasses, including tall grasses and grassy meadows.
+2. Hay: Llamas also eat hay, which is a dry, compressed form of grass or other plants.
+3. Alfalfa: Alfalfa is a legume that is commonly used as a hay substitute in llama feed.
+4. Other plants: Llamas will also eat other plants, such as clover, dandelions, and wild grasses.
+
+It's worth noting that the specific diet of llamas can vary depending on factors such as the breed,
+```
+
+&nbsp;
+**Pro tip**
+
+For up to a 4× speed-up, replace
+
+```python
+model.to(device)
+```
+
+with
+
+```python
+model = torch.compile(model)
+model.to(device)
+```
+
+Note: the speed-up takes effect after the first `generate` call.
+

pkg/llms_from_scratch/README.md

@@ -109,5 +109,13 @@ from llms_from_scratch.ch07 import (
 from llms_from_scratch.appendix_a import NeuralNetwork, ToyDataset
 
 from llms_from_scratch.appendix_d import find_highest_gradient, train_model
+
+from llms_from_scratch.llama3 import (
+    Llama3Model,
+    Llama3Tokenizer,
+    ChatFormat,
+    clean_text
+)
 ```
 
+(For the `llms_from_scratch.llama3` usage information, please see [this bonus section](../../ch05/07_gpt_to_llama/README.md).

pkg/llms_from_scratch/llama3.py

@@ -0,0 +1,377 @@
+# 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 os
+from pathlib import Path
+
+import torch
+import torch.nn as nn
+
+import tiktoken
+from tiktoken.load import load_tiktoken_bpe
+
+
+LLAMA32_CONFIG_1B = {
+    "vocab_size": 128_256,           # Vocabulary size
+    "context_length": 8192,          # Maximum context length to use (reduced to save memory)
+    "orig_context_length": 131_072,  # Context length that was used to train the model
+    "emb_dim": 2048,                 # Embedding dimension
+    "n_heads": 32,                   # Number of attention heads
+    "n_layers": 16,                  # Number of layers
+    "hidden_dim": 8192,              # Size of the intermediate dimension in FeedForward
+    "n_kv_groups": 8,                # Key-Value groups for grouped-query attention
+    "rope_base": 500_000.0,          # The base in RoPE's "theta"
+    "dtype": torch.bfloat16,         # Lower-precision dtype to reduce memory usage
+    "rope_freq": {                   # RoPE frequency scaling
+        "factor": 32.0,
+        "low_freq_factor": 1.0,
+        "high_freq_factor": 4.0,
+        "original_context_length": 8192,
+    }
+}
+
+LLAMA32_CONFIG_3B = {
+    "vocab_size": 128_256,           # Vocabulary size
+    "context_length": 8192,          # Maximum context length to use (reduced to save memory)
+    "orig_context_length": 131_072,  # Context length that was used to train the model
+    "emb_dim": 3072,                 # Embedding dimension
+    "n_heads": 24,                   # Number of attention heads
+    "n_layers": 28,                  # Number of layers
+    "hidden_dim": 8192,              # Size of the intermediate dimension in FeedForward
+    "n_kv_groups": 8,                # Key-Value groups for grouped-query attention
+    "rope_base": 500_000.0,          # The base in RoPE's "theta"
+    "dtype": torch.bfloat16,         # Lower-precision dtype to reduce memory usage
+    "rope_freq": {                   # RoPE frequency scaling
+        "factor": 32.0,
+        "low_freq_factor": 1.0,
+        "high_freq_factor": 4.0,
+        "original_context_length": 8192,
+    }
+}
+
+
+class Llama3Model(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+
+        # Main model parameters
+        self.tok_emb = nn.Embedding(cfg["vocab_size"], cfg["emb_dim"], dtype=cfg["dtype"])
+
+        self.trf_blocks = nn.ModuleList(  # ModuleList since Sequential can only accept one input, and we need `x, mask, cos, sin`
+            [TransformerBlock(cfg) for _ in range(cfg["n_layers"])]
+        )
+
+        self.final_norm = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])
+        self.out_head = nn.Linear(cfg["emb_dim"], cfg["vocab_size"], bias=False, dtype=cfg["dtype"])
+
+        # Reusuable utilities
+        self.register_buffer("mask", torch.triu(torch.ones(cfg["context_length"], cfg["context_length"]), diagonal=1).bool())
+
+        if cfg["orig_context_length"] != cfg["context_length"]:
+            cfg["rope_base"] = rescale_theta(
+                            cfg["rope_base"],
+                            cfg["orig_context_length"],
+                            cfg["context_length"]
+                        )
+        cos, sin = compute_rope_params(
+            head_dim=cfg["emb_dim"] // cfg["n_heads"],
+            theta_base=cfg["rope_base"],
+            context_length=cfg["context_length"],
+            freq_config=cfg["rope_freq"]
+        )
+        self.register_buffer("cos", cos, persistent=False)
+        self.register_buffer("sin", sin, persistent=False)
+        self.cfg = cfg
+
+    def forward(self, in_idx):
+        # Forward pass
+        tok_embeds = self.tok_emb(in_idx)
+        x = tok_embeds
+
+        for block in self.trf_blocks:
+            x = block(x, self.mask, self.cos, self.sin)
+        x = self.final_norm(x)
+        logits = self.out_head(x.to(self.cfg["dtype"]))
+        return logits
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.att = GroupedQueryAttention(
+            d_in=cfg["emb_dim"],
+            d_out=cfg["emb_dim"],
+            num_heads=cfg["n_heads"],
+            num_kv_groups=cfg["n_kv_groups"],
+            dtype=cfg["dtype"]
+        )
+        self.ff = FeedForward(cfg)
+        self.norm1 = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])
+        self.norm2 = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])
+
+    def forward(self, x, mask, cos, sin):
+        # Shortcut connection for attention block
+        shortcut = x
+        x = self.norm1(x)
+        x = self.att(x, mask, cos, sin)  # Shape [batch_size, num_tokens, emb_size]
+        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 = x + shortcut  # Add the original input back
+
+        return x
+
+
+class FeedForward(nn.Module):
+    def __init__(self, cfg):
+        super().__init__()
+        self.fc1 = nn.Linear(cfg["emb_dim"], cfg["hidden_dim"], dtype=cfg["dtype"], bias=False)
+        self.fc2 = nn.Linear(cfg["emb_dim"], cfg["hidden_dim"], dtype=cfg["dtype"], bias=False)
+        self.fc3 = nn.Linear(cfg["hidden_dim"], cfg["emb_dim"], dtype=cfg["dtype"], bias=False)
+
+    def forward(self, x):
+        x_fc1 = self.fc1(x)
+        x_fc2 = self.fc2(x)
+        x = nn.functional.silu(x_fc1) * x_fc2
+        return self.fc3(x)
+
+
+class GroupedQueryAttention(nn.Module):
+    def __init__(
+            self, d_in, d_out, num_heads,
+            num_kv_groups,
+            dtype=None
+    ):
+        super().__init__()
+        assert d_out % num_heads == 0, "d_out must be divisible by num_heads"
+        assert num_heads % num_kv_groups == 0, "num_heads must be divisible by num_kv_groups"
+
+        self.d_out = d_out
+        self.num_heads = num_heads
+        self.head_dim = d_out // num_heads
+
+        self.W_key = nn.Linear(d_in, num_kv_groups * self.head_dim, bias=False, dtype=dtype)
+        self.W_value = nn.Linear(d_in, num_kv_groups * self.head_dim, bias=False, dtype=dtype)
+        self.num_kv_groups = num_kv_groups
+        self.group_size = num_heads // num_kv_groups
+
+        self.W_query = nn.Linear(d_in, d_out, bias=False, dtype=dtype)
+        self.out_proj = nn.Linear(d_out, d_out, bias=False, dtype=dtype)
+
+    def forward(self, x, mask, cos, sin):
+        b, num_tokens, d_in = x.shape
+
+        queries = self.W_query(x)  # Shape: (b, num_tokens, d_out)
+        keys = self.W_key(x)  # Shape: (b, num_tokens, num_kv_groups * head_dim)
+        values = self.W_value(x)  # Shape: (b, num_tokens, num_kv_groups * head_dim)
+
+        # Reshape queries, keys, and values
+        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
+        keys = keys.view(b, num_tokens, self.num_kv_groups, self.head_dim)
+        values = values.view(b, num_tokens, self.num_kv_groups, self.head_dim)
+
+        # Transpose keys, values, and queries
+        keys = keys.transpose(1, 2)  # Shape: (b, num_heads, num_tokens, head_dim)
+        values = values.transpose(1, 2)  # Shape: (b, num_heads, num_tokens, head_dim)
+        queries = queries.transpose(1, 2)  # Shape: (b, num_query_groups, num_tokens, head_dim)
+
+        # Apply RoPE
+        keys = apply_rope(keys, cos, sin)
+        queries = apply_rope(queries, cos, sin)
+
+        # Expand keys and values to match the number of heads
+        # Shape: (b, num_heads, num_tokens, head_dim)
+        keys = keys.repeat_interleave(self.group_size, dim=1)  # Shape: (b, num_heads, num_tokens, head_dim)
+        values = values.repeat_interleave(self.group_size, dim=1)  # Shape: (b, num_heads, num_tokens, head_dim)
+        # For example, before repeat_interleave along dim=1 (query groups):
+        #   [K1, K2]
+        # After repeat_interleave (each query group is repeated group_size times):
+        #   [K1, K1, K2, K2]
+        # If we used regular repeat instead of repeat_interleave, we'd get:
+        #   [K1, K2, K1, K2]
+
+        # Compute scaled dot-product attention (aka self-attention) with a causal mask
+        # Shape: (b, num_heads, num_tokens, num_tokens)
+        attn_scores = queries @ keys.transpose(2, 3)  # Dot product for each head
+
+        # Use the mask to fill attention scores
+        attn_scores = attn_scores.masked_fill(mask[:num_tokens, :num_tokens], -torch.inf)
+
+        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
+        assert keys.shape[-1] == self.head_dim
+
+        # 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.reshape(b, num_tokens, self.d_out)
+        context_vec = self.out_proj(context_vec)  # optional projection
+
+        return context_vec
+
+
+def compute_rope_params(head_dim, theta_base=10_000, context_length=4096, freq_config=None, dtype=torch.float32):
+    assert head_dim % 2 == 0, "Embedding dimension must be even"
+
+    # Compute the inverse frequencies
+    inv_freq = 1.0 / (theta_base ** (torch.arange(0, head_dim, 2, dtype=dtype)[: (head_dim // 2)].float() / head_dim))
+
+    # Frequency adjustments
+    if freq_config is not None:
+        low_freq_wavelen = freq_config["original_context_length"] / freq_config["low_freq_factor"]
+        high_freq_wavelen = freq_config["original_context_length"] / freq_config["high_freq_factor"]
+
+        wavelen = 2 * torch.pi / inv_freq
+
+        inv_freq_llama = torch.where(
+            wavelen > low_freq_wavelen, inv_freq / freq_config["factor"], inv_freq
+        )
+
+        smooth_factor = (freq_config["original_context_length"] / wavelen - freq_config["low_freq_factor"]) / (
+            freq_config["high_freq_factor"] - freq_config["low_freq_factor"]
+        )
+
+        smoothed_inv_freq = (
+            (1 - smooth_factor) * (inv_freq / freq_config["factor"]) + smooth_factor * inv_freq
+        )
+
+        is_medium_freq = (wavelen <= low_freq_wavelen) & (wavelen >= high_freq_wavelen)
+        inv_freq_llama = torch.where(is_medium_freq, smoothed_inv_freq, inv_freq_llama)
+        inv_freq = inv_freq_llama
+
+    # Generate position indices
+    positions = torch.arange(context_length, dtype=dtype)
+
+    # Compute the angles
+    angles = positions[:, None] * inv_freq[None, :]  # Shape: (context_length, head_dim // 2)
+
+    # Expand angles to match the head_dim
+    angles = torch.cat([angles, angles], dim=1)  # Shape: (context_length, head_dim)
+
+    # Precompute sine and cosine
+    cos = torch.cos(angles)
+    sin = torch.sin(angles)
+
+    return cos, sin
+
+
+def apply_rope(x, cos, sin):
+    # x: (batch_size, num_heads, seq_len, head_dim)
+    batch_size, num_heads, seq_len, head_dim = x.shape
+    assert head_dim % 2 == 0, "Head dimension must be even"
+
+    # Split x into first half and second half
+    x1 = x[..., : head_dim // 2]  # First half
+    x2 = x[..., head_dim // 2:]  # Second half
+
+    # Adjust sin and cos shapes
+    cos = cos[:seq_len, :].unsqueeze(0).unsqueeze(0)  # Shape: (1, 1, seq_len, head_dim)
+    sin = sin[:seq_len, :].unsqueeze(0).unsqueeze(0)
+
+    # Apply the rotary transformation
+    rotated = torch.cat((-x2, x1), dim=-1)
+    x_rotated = (x * cos) + (rotated * sin)
+
+    # It's ok to use lower-precision after applying cos and sin rotation
+    return x_rotated.to(dtype=x.dtype)
+
+
+def rescale_theta(theta_old, context_length_old, context_length_new):
+    scaling_factor = context_length_new / context_length_old
+    theta_new = theta_old * scaling_factor
+    return theta_new
+
+
+##########################################
+# Tokenizer
+##########################################
+
+
+class Llama3Tokenizer:
+    def __init__(self, model_path):
+        assert os.path.isfile(model_path), f"Model file {model_path} not found"
+        mergeable_ranks = load_tiktoken_bpe(model_path)
+
+        self.special_tokens = {
+            "<|begin_of_text|>": 128000,
+            "<|end_of_text|>": 128001,
+            "<|start_header_id|>": 128006,
+            "<|end_header_id|>": 128007,
+            "<|eot_id|>": 128009,
+        }
+        self.special_tokens.update({
+            f"<|reserved_{i}|>": 128002 + i for i in range(256) if (128002 + i) not in self.special_tokens.values()
+        })
+
+        self.model = tiktoken.Encoding(
+            name=Path(model_path).name,
+            pat_str=r"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+",
+            mergeable_ranks=mergeable_ranks,
+            special_tokens=self.special_tokens
+        )
+
+    def encode(self, text, bos=False, eos=False, allowed_special=set(), disallowed_special=()):
+        if bos:
+            tokens = [self.special_tokens["<|begin_of_text|>"]]
+        else:
+            tokens = []
+
+        tokens += self.model.encode(text, allowed_special=allowed_special, disallowed_special=disallowed_special)
+
+        if eos:
+            tokens.append(self.special_tokens["<|end_of_text|>"])
+        return tokens
+
+    def decode(self, tokens):
+        return self.model.decode(tokens)
+
+
+class ChatFormat:
+    def __init__(self, tokenizer):
+        self.tokenizer = tokenizer
+
+    def encode_header(self, message):
+        tokens = []
+        tokens.append(self.tokenizer.special_tokens["<|start_header_id|>"])
+        tokens.extend(self.tokenizer.encode(message["role"], bos=False, eos=False))
+        tokens.append(self.tokenizer.special_tokens["<|end_header_id|>"])
+        tokens.extend(self.tokenizer.encode("\n\n", bos=False, eos=False))
+        return tokens
+
+    def encode(self, text, allowed_special=None):
+        message = {
+            "role": "user",
+            "content": text
+        }
+
+        tokens = self.encode_header(message)
+        tokens.extend(
+            self.tokenizer.encode(
+                message["content"].strip(),
+                bos=False,
+                eos=False,
+                allowed_special=allowed_special
+            )
+        )
+        tokens.append(self.tokenizer.special_tokens["<|eot_id|>"])
+        return tokens
+
+    def decode(self, token_ids):
+        return self.tokenizer.decode(token_ids)
+
+
+def clean_text(text, header_end="assistant<|end_header_id|>\n\n"):
+    # Find the index of the first occurrence of "<|end_header_id|>"
+    index = text.find(header_end)
+
+    if index != -1:
+        # Return the substring starting after "<|end_header_id|>"
+        return text[index + len(header_end):].strip()  # Strip removes leading/trailing whitespace
+    else:
+        # If the token is not found, return the original text
+        return text

pkg/llms_from_scratch/tests/test_llama3.py

@@ -0,0 +1,147 @@
+# 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
+
+from llms_from_scratch.ch04 import generate_text_simple
+from llms_from_scratch.llama3 import (
+    compute_rope_params,
+    apply_rope,
+    rescale_theta,
+    LLAMA32_CONFIG_1B,
+    Llama3Model
+)
+
+import importlib
+import pytest
+import tiktoken
+import torch
+
+
+transformers_installed = importlib.util.find_spec("transformers") is not None
+
+
+@pytest.mark.skipif(not transformers_installed, reason="transformers not installed")
+def test_rope():
+
+    from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding, apply_rotary_pos_emb
+
+    # Settings
+    batch_size = 1
+    context_len = 8192
+    num_heads = 4
+    head_dim = 16
+    rope_theta = 500_000
+
+    rope_config = {
+        "factor": 8.0,
+        "low_freq_factor": 1.0,
+        "high_freq_factor": 4.0,
+        "original_context_length": 8192,
+    }
+
+    # Instantiate RoPE parameters
+    cos, sin = compute_rope_params(
+        head_dim=head_dim,
+        theta_base=rope_theta,
+        context_length=context_len,
+        freq_config=rope_config,
+    )
+
+    # Dummy query and key tensors
+    torch.manual_seed(123)
+    queries = torch.randn(batch_size, num_heads, context_len, head_dim)
+    keys = torch.randn(batch_size, num_heads, context_len, head_dim)
+
+    # Apply rotary position embeddings
+    queries_rot = apply_rope(queries, cos, sin)
+    keys_rot = apply_rope(keys, cos, sin)
+
+    # Generate reference RoPE via HF
+    hf_rope_params = {
+        "factor": 8.0,
+        "low_freq_factor": 1.0,
+        "high_freq_factor": 4.0,
+        "original_max_position_embeddings": 8192,
+        "rope_type": "llama3"
+    }
+
+    class RoPEConfig:
+        rope_type = "llama3"
+        rope_scaling = hf_rope_params
+        factor = 1.0
+        dim: int = head_dim
+        rope_theta = 500_000
+        max_position_embeddings: int = 8192
+        hidden_size = head_dim * num_heads
+        num_attention_heads = num_heads
+
+    config = RoPEConfig()
+
+    rot_emb = LlamaRotaryEmbedding(config=config)
+    position_ids = torch.arange(context_len, dtype=torch.long).unsqueeze(0)
+    ref_cos, ref_sin = rot_emb(queries, position_ids)
+    ref_queries_rot, ref_keys_rot = apply_rotary_pos_emb(queries, keys, ref_cos, ref_sin)
+
+    torch.testing.assert_close(sin, ref_sin.squeeze(0))
+    torch.testing.assert_close(cos, ref_cos.squeeze(0))
+    torch.testing.assert_close(keys_rot, ref_keys_rot)
+    torch.testing.assert_close(queries_rot, ref_queries_rot)
+
+
+GPT_CONFIG_124M = {
+    "vocab_size": 50257,     # Vocabulary size
+    "context_length": 1024,  # Context length
+    "emb_dim": 768,          # Embedding dimension
+    "n_heads": 12,           # Number of attention heads
+    "n_layers": 12,          # Number of layers
+    "drop_rate": 0.1,        # Dropout rate
+    "qkv_bias": False        # Query-Key-Value bias
+}
+
+
+def test_rescale():
+
+    new_theta = rescale_theta(
+        theta_old=500_000.,
+        context_length_old=131_072,
+        context_length_new=8192
+    )
+    assert new_theta == 31250.
+
+    old_theta = rescale_theta(
+        theta_old=new_theta,
+        context_length_old=8192,
+        context_length_new=131_072
+    )
+    assert old_theta == 500_000.
+
+
+@pytest.mark.parametrize("ModelClass", [Llama3Model])
+def test_gpt_model_variants(ModelClass):
+    torch.manual_seed(123)
+    model = ModelClass(LLAMA32_CONFIG_1B)
+    model.eval()
+
+    start_context = "Hello, I am"
+
+    tokenizer = tiktoken.get_encoding("gpt2")
+    encoded = tokenizer.encode(start_context)
+    encoded_tensor = torch.tensor(encoded).unsqueeze(0)
+
+    print(f"\n{50*'='}\n{22*' '}IN\n{50*'='}")
+    print("\nInput text:", start_context)
+    print("Encoded input text:", encoded)
+    print("encoded_tensor.shape:", encoded_tensor.shape)
+
+    out = generate_text_simple(
+        model=model,
+        idx=encoded_tensor,
+        max_new_tokens=10,
+        context_size=LLAMA32_CONFIG_1B["context_length"]
+    )
+    expect = torch.tensor([
+        [15496,     11,    314,    716,  78563,  89362,  19616, 115725, 114917,
+         97198,  60342,  19108, 100752,  98969]
+    ])
+    assert torch.equal(expect, out)

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "llms-from-scratch"
-version = "1.0.2"
+version = "1.0.5"
 description = "Implement a ChatGPT-like LLM in PyTorch from scratch, step by step"
 readme = "README.md"
 requires-python = ">=3.10"