From d93fbbd4b9c8f9f1cb3a8b610b5188646675d70a Mon Sep 17 00:00:00 2001 From: rasbt Date: Thu, 23 May 2024 20:35:41 -0500 Subject: [PATCH] flops analysis --- ch04/01_main-chapter-code/README.md | 2 +- ch04/01_main-chapter-code/ch04.ipynb | 2 +- ch04/02_performance-analysis/README.md | 5 + .../flops-analysis.ipynb | 154 ++++++++++ .../previous_chapters.py | 279 ++++++++++++++++++ .../requirements-extra.txt | 1 + ch04/README.md | 3 +- 7 files changed, 443 insertions(+), 3 deletions(-) create mode 100644 ch04/02_performance-analysis/README.md create mode 100644 ch04/02_performance-analysis/flops-analysis.ipynb create mode 100644 ch04/02_performance-analysis/previous_chapters.py create mode 100644 ch04/02_performance-analysis/requirements-extra.txt diff --git a/ch04/01_main-chapter-code/README.md b/ch04/01_main-chapter-code/README.md index 7d22944..e9eb0cb 100644 --- a/ch04/01_main-chapter-code/README.md +++ b/ch04/01_main-chapter-code/README.md @@ -1,4 +1,4 @@ -# Chapter 4: Implementing a GPT model from Scratch To Generate Text +# Chapter 4: Implementing a GPT Model from Scratch To Generate Text - [ch04.ipynb](ch04.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 [ch04.ipynb](ch04.ipynb) to create the GPT model diff --git a/ch04/01_main-chapter-code/ch04.ipynb b/ch04/01_main-chapter-code/ch04.ipynb index e1a67f6..4fca3f5 100644 --- a/ch04/01_main-chapter-code/ch04.ipynb +++ b/ch04/01_main-chapter-code/ch04.ipynb @@ -1489,7 +1489,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.4" } }, "nbformat": 4, diff --git a/ch04/02_performance-analysis/README.md b/ch04/02_performance-analysis/README.md new file mode 100644 index 0000000..d3eee11 --- /dev/null +++ b/ch04/02_performance-analysis/README.md @@ -0,0 +1,5 @@ +# Chapter 4: Implementing a GPT Model from Scratch To Generate Text + +- [flops-analysis.ipynb](flops-analysis.ipynb) analyses the floating point operations per second (FLOPS) of the GPT model(s) implemented in the main chapter. +- [previous_chapters.py](previous_chapters.py) is a Python module containing the `GPTModel` code we implemented in chapter 4 and other code implemented in previous chapters, which we import in the analysis notebook. +- `requirements-extra.txt` includes additional Python libraries that need to be installed (via `pip install -r requirements-extra.txt`. \ No newline at end of file diff --git a/ch04/02_performance-analysis/flops-analysis.ipynb b/ch04/02_performance-analysis/flops-analysis.ipynb new file mode 100644 index 0000000..12447d8 --- /dev/null +++ b/ch04/02_performance-analysis/flops-analysis.ipynb @@ -0,0 +1,154 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "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", + "metadata": {}, + "source": [ + "## FLOPS Analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- FLOPs (Floating Point Operations Per Second) measure the computational complexity of neural network models by counting the number of floating-point operations executed\n", + "- High FLOPs indicate more intensive computation and energy consumption" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# pip install -r requirements-extra.txt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "thop version: 0.1.1-2209072238\n", + "torch version: 2.2.2\n", + "tiktoken version: 0.5.1\n" + ] + } + ], + "source": [ + "from importlib.metadata import version\n", + "\n", + "import matplotlib\n", + "import tiktoken\n", + "import torch\n", + "\n", + "print(\"thop version:\", version(\"thop\"))\n", + "print(\"torch version:\", version(\"torch\"))\n", + "print(\"tiktoken version:\", version(\"tiktoken\"))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GerIdRMXd6g9", + "outputId": "ccdd5c71-d221-4a84-f9bc-09557e77162d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "gpt-small (124M) : 5.1e+11 FLOPS\n", + "gpt-medium (355M) : 1.4e+12 FLOPS\n", + "gpt-large (774M) : 3.2e+12 FLOPS\n", + "gpt-xl (1558M) : 6.4e+12 FLOPS\n" + ] + } + ], + "source": [ + "import torch\n", + "from thop import profile\n", + "\n", + "from previous_chapters import GPTModel\n", + "\n", + "\n", + "BASE_CONFIG = {\n", + " \"vocab_size\": 50257, # Vocabulary size\n", + " \"context_length\": 1024, # Context length\n", + " \"drop_rate\": 0.0, # Dropout rate\n", + " \"qkv_bias\": True # Query-key-value bias\n", + "}\n", + "\n", + "model_configs = {\n", + " \"gpt-small (124M)\": {\"emb_dim\": 768, \"n_layers\": 12, \"n_heads\": 12},\n", + " \"gpt-medium (355M)\": {\"emb_dim\": 1024, \"n_layers\": 24, \"n_heads\": 16},\n", + " \"gpt-large (774M)\": {\"emb_dim\": 1280, \"n_layers\": 36, \"n_heads\": 20},\n", + " \"gpt-xl (1558M)\": {\"emb_dim\": 1600, \"n_layers\": 48, \"n_heads\": 25},\n", + "}\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "input_tensor = torch.randint(0, 50257, (2, 1024)).to(device)\n", + "\n", + "for size in model_configs:\n", + " BASE_CONFIG.update(model_configs[size])\n", + " \n", + " model = GPTModel(BASE_CONFIG).bfloat16()\n", + " model.to(device)\n", + "\n", + " # MACS = multiply-accumulate operations\n", + " # MACS are typically counted as two FLOPS (one multiply and one accumulate)\n", + " macs, params = profile(model, inputs=(input_tensor,), verbose=False)\n", + " flops = 2*macs\n", + " print(f\"{size:18}: {flops:.1e} FLOPS\")\n", + " \n", + " del model\n", + " torch.cuda.empty_cache()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "A100", + "machine_shape": "hm", + "provenance": [] + }, + "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": 4 +} diff --git a/ch04/02_performance-analysis/previous_chapters.py b/ch04/02_performance-analysis/previous_chapters.py new file mode 100644 index 0000000..9b05743 --- /dev/null +++ b/ch04/02_performance-analysis/previous_chapters.py @@ -0,0 +1,279 @@ +# 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 +# +# 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.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, num_workers=0): + # 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, num_workers=0) + + return dataloader + + +##################################### +# Chapter 3 +##################################### +class MultiHeadAttention(nn.Module): + def __init__(self, d_in, d_out, context_length, 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(context_length, context_length), 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"]), + ) + + 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"]) + 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): + # 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_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"])]) + + 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 + + +if __name__ == "__main__": + + 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 + } + + torch.manual_seed(123) + model = GPTModel(GPT_CONFIG_124M) + model.eval() # disable dropout + + 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=GPT_CONFIG_124M["context_length"] + ) + decoded_text = tokenizer.decode(out.squeeze(0).tolist()) + + print(f"\n\n{50*'='}\n{22*' '}OUT\n{50*'='}") + print("\nOutput:", out) + print("Output length:", len(out[0])) + print("Output text:", decoded_text) diff --git a/ch04/02_performance-analysis/requirements-extra.txt b/ch04/02_performance-analysis/requirements-extra.txt new file mode 100644 index 0000000..dc84216 --- /dev/null +++ b/ch04/02_performance-analysis/requirements-extra.txt @@ -0,0 +1 @@ +thop \ No newline at end of file diff --git a/ch04/README.md b/ch04/README.md index 5a78490..d96ecd6 100644 --- a/ch04/README.md +++ b/ch04/README.md @@ -1,3 +1,4 @@ # Chapter 4: Implementing a GPT Model from Scratch to Generate Text -- [01_main-chapter-code](01_main-chapter-code) contains the main chapter code. \ No newline at end of file +- [01_main-chapter-code](01_main-chapter-code) contains the main chapter code. +- [02_performance-analysis](02_performance-analysis) contains optional code analyzing the performance of the GPT model(s) implemented in the main chapter. \ No newline at end of file