From 9638cbba09697ab06b44b0d9c3c15ea089a4053e Mon Sep 17 00:00:00 2001 From: Sebastian Raschka Date: Wed, 20 Mar 2024 08:43:18 -0500 Subject: [PATCH] Add alternative weight loading strategy as backup (#82) --- .gitignore | 1 + ch05/01_main-chapter-code/README.md | 7 + ch05/01_main-chapter-code/generate.py | 9 +- ch05/02_alternative_weight_loading/README.md | 5 + .../previous_chapters.py | 287 ++++++++++++++++ .../weight-loading-hf-transformers.ipynb | 312 ++++++++++++++++++ .../hparam_search.py | 0 .../previous_chapters.py | 0 .../the-verdict.txt | 0 ch05/README.md | 6 +- 10 files changed, 621 insertions(+), 6 deletions(-) create mode 100644 ch05/01_main-chapter-code/README.md create mode 100644 ch05/02_alternative_weight_loading/README.md create mode 100644 ch05/02_alternative_weight_loading/previous_chapters.py create mode 100644 ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb rename ch05/{02_bonus_hparam_tuning => 04_bonus_hparam_tuning}/hparam_search.py (100%) rename ch05/{02_bonus_hparam_tuning => 04_bonus_hparam_tuning}/previous_chapters.py (100%) rename ch05/{02_bonus_hparam_tuning => 04_bonus_hparam_tuning}/the-verdict.txt (100%) diff --git a/.gitignore b/.gitignore index 3b34f1bd..aaf89e57 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,4 @@ +ch05/02_alternative_weight_loading/checkpoints ch05/01_main-chapter-code/the-verdict.txt .DS_Store diff --git a/ch05/01_main-chapter-code/README.md b/ch05/01_main-chapter-code/README.md new file mode 100644 index 00000000..98c0164b --- /dev/null +++ b/ch05/01_main-chapter-code/README.md @@ -0,0 +1,7 @@ +# Chapter 5: Pretraining on Unlabeled Data + +- [ch05.ipynb](ch05.ipynb) contains all the code as it appears in the chapter +- [previous_chapters.py](previous_chapters.py) is a Python module that contains the `MultiHeadAttention` module from the previous chapter, which we import in [ch05.ipynb](ch05.ipynb) to pretrain the GPT model +- [train.py](train.py) is a standalone Python script file with the code that we implemented in [ch05.ipynb](ch05.ipynb) to train the GPT model +- [generate.py](generate.py) is a standalone Python script file with the code that we implemented in [ch05.ipynb](ch05.ipynb) to load and use the pretrained model weights from OpenAI + diff --git a/ch05/01_main-chapter-code/generate.py b/ch05/01_main-chapter-code/generate.py index 29a39d4a..8adb728f 100644 --- a/ch05/01_main-chapter-code/generate.py +++ b/ch05/01_main-chapter-code/generate.py @@ -199,16 +199,17 @@ def main(gpt_config, input_prompt, model_size): gpt = GPTModel(gpt_config) load_weights_into_gpt(gpt, params) gpt.to(device) + gpt.eval() tokenizer = tiktoken.get_encoding("gpt2") token_ids = generate( model=gpt, idx=text_to_token_ids(input_prompt, tokenizer), - max_new_tokens=65, + max_new_tokens=30, context_size=gpt_config["ctx_len"], - top_k=50, - temperature=1.5 + top_k=1, + temperature=1.0 ) print("Output text:\n", token_ids_to_text(token_ids, tokenizer)) @@ -219,7 +220,7 @@ def main(gpt_config, input_prompt, model_size): torch.manual_seed(123) CHOOSE_MODEL = "gpt2-small" - INPUT_PROMPT = "Every effort moves you" + INPUT_PROMPT = "Every effort moves" BASE_CONFIG = { "vocab_size": 50257, # Vocabulary size diff --git a/ch05/02_alternative_weight_loading/README.md b/ch05/02_alternative_weight_loading/README.md new file mode 100644 index 00000000..e00d0203 --- /dev/null +++ b/ch05/02_alternative_weight_loading/README.md @@ -0,0 +1,5 @@ +# Alternative Weight Loading + +This folder contains alternative weight loading strategies in case the weights become unavailable from Open AI. + +- [weight-loading-hf-transformers.ipynb](weight-loading-hf-transformers.ipynb): contains code to load the weights from the Hugging Face Model Hub via the `transformers` library \ No newline at end of file diff --git a/ch05/02_alternative_weight_loading/previous_chapters.py b/ch05/02_alternative_weight_loading/previous_chapters.py new file mode 100644 index 00000000..d772e86d --- /dev/null +++ b/ch05/02_alternative_weight_loading/previous_chapters.py @@ -0,0 +1,287 @@ +# This file collects all the relevant code that we covered thus far +# throughout Chapters 2-4. +# This file can be run as a standalone script. + +import tiktoken +import torch +import torch.nn as nn +from torch.utils.data import Dataset, DataLoader + +##################################### +# Chapter 2 +##################################### + + +class GPTDatasetV1(Dataset): + def __init__(self, txt, tokenizer, max_length, stride): + self.tokenizer = tokenizer + self.input_ids = [] + self.target_ids = [] + + # Tokenize the entire text + token_ids = tokenizer.encode(txt) + + # Use a sliding window to chunk the book into overlapping sequences of max_length + for i in range(0, len(token_ids) - max_length, stride): + input_chunk = token_ids[i:i + max_length] + target_chunk = token_ids[i + 1: i + max_length + 1] + self.input_ids.append(torch.tensor(input_chunk)) + self.target_ids.append(torch.tensor(target_chunk)) + + def __len__(self): + return len(self.input_ids) + + def __getitem__(self, idx): + return self.input_ids[idx], self.target_ids[idx] + + +def create_dataloader_v1(txt, batch_size=4, max_length=256, + stride=128, shuffle=True, drop_last=True): + # Initialize the tokenizer + tokenizer = tiktoken.get_encoding("gpt2") + + # Create dataset + dataset = GPTDatasetV1(txt, tokenizer, max_length, stride) + + # Create dataloader + dataloader = DataLoader( + dataset, batch_size=batch_size, shuffle=shuffle, drop_last=drop_last) + + return dataloader + + +##################################### +# Chapter 3 +##################################### +class MultiHeadAttention(nn.Module): + def __init__(self, d_in, d_out, block_size, dropout, num_heads, qkv_bias=False): + super().__init__() + assert d_out % num_heads == 0, "d_out must be divisible by n_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) + self.register_buffer('mask', torch.triu(torch.ones(block_size, block_size), diagonal=1)) + + def forward(self, x): + b, num_tokens, d_in = x.shape + + keys = self.W_key(x) # Shape: (b, num_tokens, d_out) + queries = self.W_query(x) + values = self.W_value(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 = keys.view(b, num_tokens, self.num_heads, self.head_dim) + values = values.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 = keys.transpose(1, 2) + queries = queries.transpose(1, 2) + values = values.transpose(1, 2) + + # Compute scaled dot-product attention (aka self-attention) with a causal mask + attn_scores = queries @ keys.transpose(2, 3) # Dot product for each head + + # Original mask truncated to the number of tokens and converted to boolean + mask_bool = self.mask.bool()[:num_tokens, :num_tokens] + + # Use the mask to fill attention scores + attn_scores.masked_fill_(mask_bool, -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.reshape(b, num_tokens, self.d_out) + context_vec = self.out_proj(context_vec) # optional projection + + return context_vec + + +##################################### +# 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"]), + nn.Dropout(cfg["drop_rate"]) + ) + + 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"], + block_size=cfg["ctx_len"], + num_heads=cfg["n_heads"], + dropout=cfg["drop_rate"], + qkv_bias=cfg["qkv_bias"]) + self.ff = FeedForward(cfg) + self.norm1 = LayerNorm(cfg["emb_dim"]) + self.norm2 = LayerNorm(cfg["emb_dim"]) + self.drop_resid = nn.Dropout(cfg["drop_rate"]) + + def forward(self, x): + # Shortcut connection for attention block + shortcut = x + x = self.norm1(x) + x = self.att(x) # Shape [batch_size, num_tokens, emb_size] + x = self.drop_resid(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_resid(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["ctx_len"], cfg["emb_dim"]) + self.drop_emb = nn.Dropout(cfg["drop_rate"]) + + self.trf_blocks = nn.Sequential( + *[TransformerBlock(cfg) for _ in range(cfg["n_layers"])]) + + 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): + 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)) + x = tok_embeds + pos_embeds # Shape [batch_size, num_tokens, emb_size] + x = self.drop_emb(x) + x = self.trf_blocks(x) + x = self.final_norm(x) + logits = self.out_head(x) + return logits + + +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 + + +##################################### +# Chapter 5 +##################################### + + +def text_to_token_ids(text, tokenizer): + encoded = tokenizer.encode(text) + encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension + return encoded_tensor + + +def token_ids_to_text(token_ids, tokenizer): + flat = token_ids.squeeze(0) # remove batch dimension + return tokenizer.decode(flat.tolist()) + + +def generate(model, idx, max_new_tokens, context_size, temperature, top_k=None): + + # For-loop is the same as before: Get logits, and only focus on last time step + for _ in range(max_new_tokens): + idx_cond = idx[:, -context_size:] + with torch.no_grad(): + logits = model(idx_cond) + logits = logits[:, -1, :] + + # New: Filter logits with top_k sampling + if top_k is not None: + # Keep only top_k values + top_logits, _ = torch.topk(logits, top_k) + min_val = top_logits[:, -1] + logits = torch.where(logits < min_val, torch.tensor(float('-inf')).to(logits.device), logits) + + # New: Apply temperature scaling + if temperature > 0.0: + logits = logits / temperature + + # Apply softmax to get probabilities + probs = torch.softmax(logits, dim=-1) # (batch_size, context_len) + + # Sample from the distribution + idx_next = torch.multinomial(probs, num_samples=1) # (batch_size, 1) + + # Otherwise same as before: get idx of the vocab entry with the highest logits value + else: + idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch_size, 1) + + # Same as before: append sampled index to the running sequence + idx = torch.cat((idx, idx_next), dim=1) # (batch_size, num_tokens+1) + + return idx diff --git a/ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb b/ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb new file mode 100644 index 00000000..736c5bda --- /dev/null +++ b/ch05/02_alternative_weight_loading/weight-loading-hf-transformers.ipynb @@ -0,0 +1,312 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6d6bc54f-2b16-4b0f-be69-957eed5d112f", + "metadata": {}, + "source": [ + "\n", + "Supplementary code for \"Build a Large Language Model From Scratch\": https://www.manning.com/books/build-a-large-language-model-from-scratch by Sebastian Raschka
\n", + "Code repository: https://github.com/rasbt/LLMs-from-scratch\n", + "" + ] + }, + { + "cell_type": "markdown", + "id": "72953590-5363-4398-85ce-54bde07f3d8a", + "metadata": {}, + "source": [ + "# Bonus Code for Chapter 5" + ] + }, + { + "cell_type": "markdown", + "id": "1a4ab5ee-e7b9-45d3-a82b-a12bcfc0945a", + "metadata": {}, + "source": [ + "## Alternative Weight Loading from Hugging Face Model Hub using Transformers" + ] + }, + { + "cell_type": "markdown", + "id": "b2feea87-49f0-48b9-b925-b8f0dda4096f", + "metadata": {}, + "source": [ + "- In the main chapter, we loaded the GPT model weights directly from OpenAI\n", + "- This notebook provides alternative weight loading code to load the model weights from the [Hugging Face Model Hub](https://huggingface.co/docs/hub/en/models-the-hub) using the `transformers` Python library" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "99b77109-5215-4d07-a618-4d10eff1a488", + "metadata": {}, + "outputs": [], + "source": [ + "# pip install transformers" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b0467eff-b43c-4a38-93e8-5ed87a5fc2b1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "numpy version: 1.25.2\n", + "torch version: 2.2.1\n", + "transformers version: 4.33.2\n" + ] + } + ], + "source": [ + "from importlib.metadata import version\n", + "\n", + "pkgs = [\"numpy\", \"torch\", \"transformers\"]\n", + "for p in pkgs:\n", + " print(f\"{p} version: {version(p)}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ffc17d7d-bcd8-42ee-82a9-04fd55acf15d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/sebastian/miniforge3/envs/book/lib/python3.11/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n", + " torch.utils._pytree._register_pytree_node(\n", + "/Users/sebastian/miniforge3/envs/book/lib/python3.11/site-packages/transformers/utils/generic.py:311: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n", + " torch.utils._pytree._register_pytree_node(\n" + ] + }, + { + "data": { + "text/plain": [ + "GPT2Model(\n", + " (wte): Embedding(50257, 768)\n", + " (wpe): Embedding(1024, 768)\n", + " (drop): Dropout(p=0.1, inplace=False)\n", + " (h): ModuleList(\n", + " (0-11): 12 x GPT2Block(\n", + " (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (attn): GPT2Attention(\n", + " (c_attn): Conv1D()\n", + " (c_proj): Conv1D()\n", + " (attn_dropout): Dropout(p=0.1, inplace=False)\n", + " (resid_dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + " (mlp): GPT2MLP(\n", + " (c_fc): Conv1D()\n", + " (c_proj): Conv1D()\n", + " (act): NewGELUActivation()\n", + " (dropout): Dropout(p=0.1, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n", + ")" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from transformers import GPT2Model\n", + "\n", + "\n", + "# allowed model names\n", + "model_names = {\n", + " \"gpt2-small\": \"openai-community/gpt2\", # 124M\n", + " \"gpt2-medium\": \"openai-community/gpt2-medium\", # 355M\n", + " \"gpt2-large\": \"openai-community/gpt2-large\", # 774M\n", + " \"gpt2-xl\": \"openai-community/gpt2-xl\" # 1558M\n", + "}\n", + "\n", + "CHOOSE_MODEL = \"gpt2-small\"\n", + "\n", + "gpt_hf = GPT2Model.from_pretrained(model_names[CHOOSE_MODEL], cache_dir=\"checkpoints\")\n", + "gpt_hf.eval()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "9ea9b1bc-7881-46ad-9555-27a9cf23faa7", + "metadata": {}, + "outputs": [], + "source": [ + "BASE_CONFIG = {\n", + " \"vocab_size\": 50257, # Vocabulary size\n", + " \"ctx_len\": 1024, # Context length\n", + " \"drop_rate\": 0.0, # Dropout rate\n", + " \"qkv_bias\": True # Query-key-value bias\n", + "}\n", + "\n", + "model_configs = {\n", + " \"gpt2-small\": {\"emb_dim\": 768, \"n_layers\": 12, \"n_heads\": 12},\n", + " \"gpt2-medium\": {\"emb_dim\": 1024, \"n_layers\": 24, \"n_heads\": 16},\n", + " \"gpt2-large\": {\"emb_dim\": 1280, \"n_layers\": 36, \"n_heads\": 20},\n", + " \"gpt2-xl\": {\"emb_dim\": 1600, \"n_layers\": 48, \"n_heads\": 25},\n", + "}\n", + "\n", + "\n", + "BASE_CONFIG.update(model_configs[CHOOSE_MODEL])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4e2a4cf4-a54e-4307-9141-fb9f288e4dfa", + "metadata": {}, + "outputs": [], + "source": [ + "def assign_check(left, right):\n", + " if left.shape != right.shape:\n", + " raise ValueError(f\"Shape mismatch. Left: {left.shape}, Right: {right.shape}\")\n", + " return torch.nn.Parameter(torch.tensor(right))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "75be3077-f141-44bb-af88-62580ffd224c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "\n", + "def load_weights(gpt, gpt_hf):\n", + "\n", + " d = gpt_hf.state_dict()\n", + "\n", + " gpt.pos_emb.weight = assign_check(gpt.pos_emb.weight, d[\"wpe.weight\"])\n", + " gpt.tok_emb.weight = assign_check(gpt.tok_emb.weight, d[\"wte.weight\"])\n", + " \n", + " for b in range(BASE_CONFIG[\"n_layers\"]):\n", + " q_w, k_w, v_w = np.split(d[f\"h.{b}.attn.c_attn.weight\"], 3, axis=-1)\n", + " gpt.trf_blocks[b].att.W_query.weight = assign_check(gpt.trf_blocks[b].att.W_query.weight, q_w.T)\n", + " gpt.trf_blocks[b].att.W_key.weight = assign_check(gpt.trf_blocks[b].att.W_key.weight, k_w.T)\n", + " gpt.trf_blocks[b].att.W_value.weight = assign_check(gpt.trf_blocks[b].att.W_value.weight, v_w.T)\n", + " \n", + " q_b, k_b, v_b = np.split(d[f\"h.{b}.attn.c_attn.bias\"], 3, axis=-1)\n", + " gpt.trf_blocks[b].att.W_query.bias = assign_check(gpt.trf_blocks[b].att.W_query.bias, q_b)\n", + " gpt.trf_blocks[b].att.W_key.bias = assign_check(gpt.trf_blocks[b].att.W_key.bias, k_b)\n", + " gpt.trf_blocks[b].att.W_value.bias = assign_check(gpt.trf_blocks[b].att.W_value.bias, v_b)\n", + " \n", + " \n", + " gpt.trf_blocks[b].att.out_proj.weight = assign_check(gpt.trf_blocks[b].att.out_proj.weight, d[f\"h.{b}.attn.c_proj.weight\"].T)\n", + " gpt.trf_blocks[b].att.out_proj.bias = assign_check(gpt.trf_blocks[b].att.out_proj.bias, d[f\"h.{b}.attn.c_proj.bias\"])\n", + " \n", + " gpt.trf_blocks[b].ff.layers[0].weight = assign_check(gpt.trf_blocks[b].ff.layers[0].weight, d[f\"h.{b}.mlp.c_fc.weight\"].T)\n", + " gpt.trf_blocks[b].ff.layers[0].bias = assign_check(gpt.trf_blocks[b].ff.layers[0].bias, d[f\"h.{b}.mlp.c_fc.bias\"])\n", + " gpt.trf_blocks[b].ff.layers[2].weight = assign_check(gpt.trf_blocks[b].ff.layers[2].weight, d[f\"h.{b}.mlp.c_proj.weight\"].T)\n", + " gpt.trf_blocks[b].ff.layers[2].bias = assign_check(gpt.trf_blocks[b].ff.layers[2].bias, d[f\"h.{b}.mlp.c_proj.bias\"])\n", + " \n", + " gpt.trf_blocks[b].norm1.scale = assign_check(gpt.trf_blocks[b].norm1.scale, d[f\"h.{b}.ln_1.weight\"])\n", + " gpt.trf_blocks[b].norm1.shift = assign_check(gpt.trf_blocks[b].norm1.shift, d[f\"h.{b}.ln_1.bias\"])\n", + " gpt.trf_blocks[b].norm2.scale = assign_check(gpt.trf_blocks[b].norm2.scale, d[f\"h.{b}.ln_2.weight\"])\n", + " gpt.trf_blocks[b].norm2.shift = assign_check(gpt.trf_blocks[b].norm2.shift, d[f\"h.{b}.ln_2.bias\"])\n", + " \n", + " gpt.final_norm.scale = assign_check(gpt.final_norm.scale, d[f\"ln_f.weight\"])\n", + " gpt.final_norm.shift = assign_check(gpt.final_norm.shift, d[f\"ln_f.bias\"])\n", + " gpt.out_head.weight = assign_check(gpt.out_head.weight, d[\"wte.weight\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "cda44d37-92c0-4c19-a70a-15711513afce", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/jg/tpqyh1fd5js5wsr1d138k3n40000gn/T/ipykernel_32618/3877979348.py:4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " return torch.nn.Parameter(torch.tensor(right))\n" + ] + } + ], + "source": [ + "import torch\n", + "from previous_chapters import GPTModel\n", + "\n", + "\n", + "gpt = GPTModel(BASE_CONFIG)\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "load_weights(gpt, gpt_hf)\n", + "gpt.to(device);" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4ddd0d51-3ade-4890-9bab-d63f141d095f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Output text:\n", + " Every effort moves forward, but it's not enough.\n", + "\n", + "\"I'm not going to sit here and say, 'I'm not going to do this,'\n" + ] + } + ], + "source": [ + "import tiktoken\n", + "from previous_chapters import generate, text_to_token_ids, token_ids_to_text\n", + "\n", + "torch.manual_seed(123)\n", + "\n", + "tokenizer = tiktoken.get_encoding(\"gpt2\")\n", + "\n", + "token_ids = generate(\n", + " model=gpt,\n", + " idx=text_to_token_ids(\"Every effort moves\", tokenizer),\n", + " max_new_tokens=30,\n", + " context_size=BASE_CONFIG[\"ctx_len\"],\n", + " top_k=1,\n", + " temperature=1.0\n", + ")\n", + "\n", + "print(\"Output text:\\n\", token_ids_to_text(token_ids, tokenizer))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/ch05/02_bonus_hparam_tuning/hparam_search.py b/ch05/04_bonus_hparam_tuning/hparam_search.py similarity index 100% rename from ch05/02_bonus_hparam_tuning/hparam_search.py rename to ch05/04_bonus_hparam_tuning/hparam_search.py diff --git a/ch05/02_bonus_hparam_tuning/previous_chapters.py b/ch05/04_bonus_hparam_tuning/previous_chapters.py similarity index 100% rename from ch05/02_bonus_hparam_tuning/previous_chapters.py rename to ch05/04_bonus_hparam_tuning/previous_chapters.py diff --git a/ch05/02_bonus_hparam_tuning/the-verdict.txt b/ch05/04_bonus_hparam_tuning/the-verdict.txt similarity index 100% rename from ch05/02_bonus_hparam_tuning/the-verdict.txt rename to ch05/04_bonus_hparam_tuning/the-verdict.txt diff --git a/ch05/README.md b/ch05/README.md index f0d4faee..847c99e8 100644 --- a/ch05/README.md +++ b/ch05/README.md @@ -1,4 +1,6 @@ # Chapter 5: Pretraining on Unlabeled Data -- [01_main-chapter-code](01_main-chapter-code) contains the main chapter code. -- [02_hparam_tuning](02_hparam_tuning) contains an optional hyperparameter tuning script \ No newline at end of file +- [01_main-chapter-code](01_main-chapter-code) contains the main chapter code +- [02_alternative_weight_loading](02_alternative_weight_loading) contains code to load the GPT model weights from alternative places in case the model weights become unavailable from OpenAI +- [03_bonus_pretraining_on_gutenberg](03_bonus_pretraining_on_gutenberg) contains code to pretrain the LLM longer on the whole corpus of books from Project Gutenberg +- [04_hparam_tuning](04_hparam_tuning) contains an optional hyperparameter tuning script \ No newline at end of file