diff --git a/.gitignore b/.gitignore index be325cd..c945798 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,4 @@ karpathy* __pycache__ *.pyc experiments +solutions diff --git a/.python-version b/.python-version new file mode 100644 index 0000000..e4fba21 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/README.md b/README.md index 4ef2a56..988e4ab 100644 --- a/README.md +++ b/README.md @@ -15,6 +15,15 @@ repo is educational, so the aim is to keep the code as legible as possible. - Flexible tokenization using TikToken - Command-line interfaces for training and inference +## Roadmap + +[x] Switch to uv +[x] Make it easy to modify with a config file +[] Make it into a package +[] Create an easy to use interface +[] Create or check tokenizer interface +[] Apply SOTA optimizations + ## Requirements - Python 3.12+ diff --git a/docker/into_torch_with_sm120_5090.sh b/docker/into_torch_with_sm120_5090.sh new file mode 100644 index 0000000..2e5c71d --- /dev/null +++ b/docker/into_torch_with_sm120_5090.sh @@ -0,0 +1,8 @@ +#!/usr/bin/bash + +docker run -it \ + --gpus all \ + --ipc=host \ + -v "$(pwd)":/app \ + --entrypoint bash \ + vllm-sm120:latest diff --git a/main.py b/main.py new file mode 100644 index 0000000..4410167 --- /dev/null +++ b/main.py @@ -0,0 +1,6 @@ +def main(): + print("Hello from scratchgpt!") + + +if __name__ == "__main__": + main() diff --git a/poetry.lock b/poetry.lock deleted file mode 100644 index a52d19e..0000000 --- a/poetry.lock +++ /dev/null @@ -1,1453 +0,0 @@ -# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - 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b/poetry.toml deleted file mode 100644 index ab1033b..0000000 --- a/poetry.toml +++ /dev/null @@ -1,2 +0,0 @@ -[virtualenvs] -in-project = true diff --git a/pyproject.toml b/pyproject.toml index eab3677..c1ebc7a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,27 +1,33 @@ -[tool.poetry] +[project] name = "scratchgpt" -version = "0.1.0" -description = "" -authors = ["Aleksandr Yeganov ", "Dario Cazzani =2.3.2", + "ptflops>=0.7.5", + "pydantic-settings>=2.10.1", + "pydantic-yaml>=1.6.0", + "tiktoken>=0.11.0", + "torch>=2.8.0", + "tqdm>=4.67.1", + "types-tqdm>=4.67.0.20250809", +] -[tool.poetry.dependencies] -python = "^3.12" -torch = "^2.4" -tqdm = "^4.66" -types-tqdm = "^4.66" -ptflops = "^0.7" -numpy = "^2.1" -tiktoken = "^0.7" - -[tool.poetry.group.dev.dependencies] -pylint = "^3.0.3" -pytest = "^8.3" -bandit = "^1.7.7" -mypy = "^1.8.0" -pytest-cov = "^4.1.0" -isort = "^5.13.2" -black = "^24.2.0" +[dependency-groups] +dev = [ + "bandit>=1.8.6", + "black>=25.1.0", + "isort>=6.0.1", + "mypy>=1.17.1", + "pylint>=3.3.8", + "pytest>=8.4.1", +] [tool.isort] profile = "black" @@ -56,10 +62,10 @@ asyncio_mode = "auto" python_version = "3.12" [build-system] -requires = ["poetry-core"] -build-backend = "poetry.core.masonry.api" +requires = ["hatchling"] +build-backend = "hatchling.build" -[tool.poetry.scripts] +[project.scripts] train = "scratchgpt.main:main" infer = "scratchgpt.infer:main" tiktoken = "scratchgpt.tokenizer.tiktoken:main" diff --git a/scratch_gpt.yaml.sample b/scratch_gpt.yaml.sample new file mode 100644 index 0000000..ea3c247 --- /dev/null +++ b/scratch_gpt.yaml.sample @@ -0,0 +1,12 @@ +architecture: + block_size: 256 + embedding_size: 256 + num_heads: 4 + num_blocks: 4 + +training: + max_epochs: 50 + learning_rate: 3e-4 + batch_size: 48 + dropout_rate: 0.2 + random_seed: 1337 diff --git a/scratchgpt/config.py b/scratchgpt/config.py new file mode 100644 index 0000000..6a0643e --- /dev/null +++ b/scratchgpt/config.py @@ -0,0 +1,72 @@ +from pydantic import Field +from pydantic_settings import ( + BaseSettings, + PydanticBaseSettingsSource, + SettingsConfigDict, + YamlConfigSettingsSource, +) + + +class ScratchGPTArchitecture(BaseSettings): + """ + All settings for training the model. + """ + + block_size: int = 256 + embedding_size: int = 384 + """ Size of the individual embeddings vector """ + num_heads: int = 6 + num_blocks: int = 6 + vocab_size: int | None = None + + model_config = SettingsConfigDict( + env_prefix="ARCHITECTURE_", + extra="allow", + ) + + +class ScratchGPTTraining(BaseSettings): + """ + All training related parameters + """ + + max_epochs: int = 50 + learning_rate: float = 3e-4 + batch_size: int = 32 + dropout_rate: float = 0.2 + random_seed: int = 1337 + + model_config = SettingsConfigDict( + env_prefix="TRAINING_", + extra="allow", + ) + + +class ScratchGPTConfig(BaseSettings): + """ + Full model config + """ + + architecture: ScratchGPTArchitecture = Field(default_factory=ScratchGPTArchitecture) + training: ScratchGPTTraining = Field(default_factory=ScratchGPTTraining) + + model_config = SettingsConfigDict( + env_prefix="SCRATCH_GPT_", + extra="allow", + ) + + @classmethod + def settings_customise_sources( + cls, + settings_cls: type[BaseSettings], + init_settings: PydanticBaseSettingsSource, + env_settings: PydanticBaseSettingsSource, + dotenv_settings: PydanticBaseSettingsSource, + file_secret_settings: PydanticBaseSettingsSource, + ) -> tuple[PydanticBaseSettingsSource, ...]: + return ( + env_settings, + init_settings, + file_secret_settings, + YamlConfigSettingsSource(settings_cls, yaml_file="scratch_gpt.yaml"), + ) diff --git a/scratchgpt/dataloader.py b/scratchgpt/dataloader.py index 16a52be..b883e4c 100644 --- a/scratchgpt/dataloader.py +++ b/scratchgpt/dataloader.py @@ -16,7 +16,6 @@ def get_text(self) -> str: class FileTextProvider(TextProvider): - def __init__(self, file_path: str) -> None: if not os.path.exists(file_path): raise ValueError(f"File path {file_path} does not exist") @@ -31,7 +30,6 @@ def get_text(self) -> str: class FolderTextProvider(TextProvider): - def __init__(self, dir_path: str) -> None: if not os.path.exists(dir_path): raise ValueError(f"Directory path {dir_path} does not exist") @@ -41,10 +39,10 @@ def __init__(self, dir_path: str) -> None: self._data = "" for root, _, files in os.walk(dir_path): + print(f"Loading data from {root}") for file_name in files: if not file_name.startswith("."): file_path = os.path.join(root, file_name) - print(f"Loading data from {file_path}") with open(file_path, "r", encoding="utf-8") as f: self._data += f.read() + "\n" # Concatenate with a # newline between files, could be the place to add diff --git a/scratchgpt/infer.py b/scratchgpt/infer.py index 535050a..bc408d0 100644 --- a/scratchgpt/infer.py +++ b/scratchgpt/infer.py @@ -1,19 +1,15 @@ import argparse +import pathlib import sys +from pydantic_yaml import parse_yaml_file_as import torch +from scratchgpt.config import ScratchGPTConfig + from .main import TransformerLanguageModel from .model_io import get_best_model_weights_path, get_tokenizer, load_model -BATCH_SIZE = 32 -BLOCK_SIZE = 256 -MAX_EPOCHS = 50 -LEARNING_RATE = 3e-4 -N_EMBED = 384 -NUM_HEADS = 6 -NUM_BLOCKS = 6 - def parse_args() -> argparse.Namespace: """ @@ -32,13 +28,13 @@ def parse_args() -> argparse.Namespace: "--experiment", help="The path to the folder where to save experiment checkpoints", required=True, - type=str, + type=pathlib.Path, ) parser.add_argument( "-m", "--max_tokens", type=int, - default=BLOCK_SIZE * 2, + default=256, help="Number of tokens you want the model produce", ) return parser.parse_args() @@ -46,24 +42,34 @@ def parse_args() -> argparse.Namespace: def main() -> None: args = parse_args() + config_file = args.experiment / "scratch_gpt.yaml" + config = parse_yaml_file_as(ScratchGPTConfig, config_file) + print(f"Using config file {config_file}: {config.model_dump_json(indent=2)}") tokenizer = get_tokenizer(args.experiment) device = torch.device(args.device) best_model_path = get_best_model_weights_path(args.experiment) - model = TransformerLanguageModel(NUM_HEADS, tokenizer.vocab_size, N_EMBED, BLOCK_SIZE, NUM_BLOCKS) + model = TransformerLanguageModel( + config=config, + device=device, + ) load_model(best_model_path, model, device) while True: - prompt = input("Tell me your prompt: ") - if prompt == "quit": - sys.exit(0) + try: + prompt = input("Tell me your prompt: ") + if prompt == "quit": + sys.exit(0) - context = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0).to(device) - generated = model.generate(context, max_new_tokens=args.max_tokens) - inferred = tokenizer.decode(generated[0].tolist()) - print(inferred) - print("-----------------------------------") + context = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0).to(device) + generated = model.generate(context, max_new_tokens=args.max_tokens) + inferred = tokenizer.decode(generated[0].tolist()) + print(inferred) + print("-----------------------------------") + except (EOFError, SystemExit, KeyboardInterrupt): + print("\n", "=" * 20, "Goodbye", "=" * 20) + break if __name__ == "__main__": diff --git a/scratchgpt/main.py b/scratchgpt/main.py index fa5ac05..9274a66 100644 --- a/scratchgpt/main.py +++ b/scratchgpt/main.py @@ -1,21 +1,20 @@ import argparse -import math import os import sys from typing import Literal +from pydantic_yaml import to_yaml_file import torch -from ptflops import get_model_complexity_info -from torch import Tensor, nn -from torch.nn import functional as F from torch.optim.adamw import AdamW from torch.optim.optimizer import Optimizer +from torch.types import Tensor from torch.utils.data import DataLoader - from tqdm import tqdm +from .config import ScratchGPTConfig from .dataloader import FileTextProvider, FolderTextProvider, TextDataset, TextProvider from .metering import AverageValueMeter +from .model.model import TransformerLanguageModel, print_model_complexity from .model_io import ( get_best_model_weights_path, get_latest_model_weights_path, @@ -24,16 +23,9 @@ save_tokenizer, ) -torch.manual_seed(1337) +config = ScratchGPTConfig() -DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") -BATCH_SIZE = 32 -BLOCK_SIZE = 256 -MAX_EPOCHS = 50 -LEARNING_RATE = 3e-4 -N_EMBED = 384 -NUM_HEADS = 6 -NUM_BLOCKS = 6 +torch.manual_seed(config.training.random_seed) def parse_args() -> argparse.Namespace: @@ -41,7 +33,13 @@ def parse_args() -> argparse.Namespace: Create CLI args parser and execute it """ parser = argparse.ArgumentParser() - parser.add_argument("-t", "--train_source", help="The file you want to train on", required=True, type=str) + parser.add_argument( + "-t", + "--train_source", + help="The file you want to train on", + required=True, + type=str, + ) parser.add_argument( "-e", "--experiment", @@ -49,172 +47,14 @@ def parse_args() -> argparse.Namespace: required=True, type=str, ) - return parser.parse_args() - - -def print_model_complexity(model: nn.Module) -> None: - total_params = sum(p.numel() for p in model.parameters()) - trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) - - print("=== MODEL COMPLEXITY ===") - print(f"Total parameters: {total_params:,}") - print(f"Trainable parameters: {trainable_params:,}") - print(f"Model size: {total_params * 4 / 1024 / 1024:.2f} MB (float32)") - - input_shape = (BLOCK_SIZE,) - - def input_constructor(input_shape): - return torch.randint(0, model._token_embedding_table.num_embeddings, - (1,) + input_shape, device=DEVICE) - - flops, params = get_model_complexity_info( - model, - input_shape, - input_constructor=input_constructor, - print_per_layer_stat=False, - as_strings=False + parser.add_argument( + "-d", + "--device", + help="What hardware you want to run the model on", + default="cuda", + choices=["cuda", "cpu"], ) - - print(f" FLOPs per forward pass: {flops:,}") - print(f"GFLOPs per forward pass: {flops / 1e9:.2f}") - - print("=========================") - - -class Head(nn.Module): - def __init__(self, embedding_size: int, block_size: int, head_size: int) -> None: - super().__init__() - - self._key = nn.Linear(embedding_size, head_size, bias=False) - self._query = nn.Linear(embedding_size, head_size, bias=False) - self._value = nn.Linear(embedding_size, head_size, bias=False) - self._dropout_factor = 0.2 - self._dropout = nn.Dropout(self._dropout_factor) - self.register_buffer("tril", torch.tril(torch.ones(block_size, block_size))) - - def forward(self, context: Tensor) -> Tensor: - B, T, C = context.shape - key = self._key(context) - query = self._query(context) - - normalization_term: float = 1.0 / math.sqrt(C) - attention_scores = query @ key.transpose(-2, -1) * normalization_term - attention_scores = attention_scores.masked_fill(self.tril[:T, :T] == 0, float("-inf")) - attention_scores = F.softmax(attention_scores, dim=-1) - - attention_scores = self._dropout(attention_scores) - - value = self._value(context) - - out: Tensor = attention_scores @ value - return out - - -class MultiHeadAttention(nn.Module): - def __init__(self, num_heads: int, embedding_size: int, block_size: int, head_size: int) -> None: - super().__init__() - self._dropout_factor = 0.2 - self._heads = nn.ModuleList(Head(embedding_size, block_size, head_size) for _ in range(num_heads)) - self._proj = nn.Linear(embedding_size, embedding_size) - self._dropout = nn.Dropout(self._dropout_factor) - - def forward(self, context: Tensor) -> Tensor: - out = torch.cat([head(context) for head in self._heads], dim=-1) - out = self._proj(out) - out = self._dropout(out) - return out - - -class FeedFoward(nn.Module): - def __init__(self, embedding_size: int) -> None: - super().__init__() - self._ffwd_multipler = 4 - self._dropout = 0.2 - - self._net = nn.Sequential( - nn.Linear(embedding_size, embedding_size * self._ffwd_multipler), - nn.ReLU(), - nn.Linear(self._ffwd_multipler * embedding_size, embedding_size), - nn.Dropout(self._dropout), - ) - - def forward(self, tensor: Tensor) -> Tensor: - out: Tensor = self._net(tensor) - return out - - -class Block(nn.Module): - def __init__( - self, - num_heads: int, - embedding_size: int, - block_size: int, - ) -> None: - super().__init__() - head_size = embedding_size // num_heads - self._self_attn_heads = MultiHeadAttention(num_heads, embedding_size, block_size, head_size) - self._ffwd = FeedFoward(embedding_size) - self._layer_norm_attention = nn.LayerNorm(embedding_size) - self._layer_norm_ffwd = nn.LayerNorm(embedding_size) - - def forward(self, tensor: Tensor) -> Tensor: - normal_tensor = self._layer_norm_attention(tensor) - - tensor = tensor + self._self_attn_heads(normal_tensor) - - normal_tensor = self._layer_norm_ffwd(tensor) - tensor = tensor + self._ffwd(normal_tensor) - return tensor - - -class TransformerLanguageModel(nn.Module): - - def __init__( - self, - num_heads: int, - vocab_size: int, - embedding_size: int, - block_size: int, - num_blocks: int, - ) -> None: - super().__init__() - self._block_size = block_size - self._token_embedding_table = nn.Embedding(vocab_size, embedding_size) - self._position_embedding_table = nn.Embedding(block_size, embedding_size) - self._blocks = nn.Sequential(*[Block(num_heads, embedding_size, block_size) for _ in range(num_blocks)]) - self._block_norm = nn.LayerNorm(embedding_size) - self._lm_head = nn.Linear(embedding_size, vocab_size) - - def forward(self, context: Tensor, targets: Tensor | None = None) -> tuple[Tensor, Tensor]: - context = context.long() - B, T = context.shape - - tok_emb = self._token_embedding_table(context) # B, T, C - pos_emb = self._position_embedding_table(torch.arange(T, device=DEVICE)) # (T, C) - x = tok_emb + pos_emb # B, T, C - x = self._blocks(x) - x = self._block_norm(x) - logits = self._lm_head(x) # (B, T, vocab_size) - - if targets is None: - loss = torch.empty(0) - else: - B, T, C = logits.shape - logits = logits.view(B * T, C) - targets = targets.view(B * T) - loss = F.cross_entropy(logits, targets) - - return logits, loss - - def generate(self, context: Tensor, max_new_tokens: int) -> Tensor: - for _ in range(max_new_tokens): - cropped_context = context[:, -self._block_size :] - logits, _loss = self(cropped_context) - logits = logits[:, -1, :] # becomes (B, C) - probs = F.softmax(logits, dim=-1) - idx_next = torch.multinomial(probs, num_samples=1) - context = torch.cat((context, idx_next), dim=1) - return context + return parser.parse_args() DatasetType = tuple[Tensor, Tensor] @@ -279,30 +119,47 @@ def get_text_provider(path: str) -> TextProvider: def main() -> None: args = parse_args() - print(f"Using the device: {DEVICE}") + + device = torch.device(args.device) + print(f"Using the device: {device}") text_provider = get_text_provider(args.train_source) tokenizer = get_tokenizer(args.experiment) + config.architecture.vocab_size = tokenizer.vocab_size + print(config) - train_dataset = TextDataset(text_provider, tokenizer, BLOCK_SIZE, "train", 0.9) - val_dataset = TextDataset(text_provider, tokenizer, BLOCK_SIZE, "validation", 0.1) + train_dataset = TextDataset(text_provider, tokenizer, config.architecture.block_size, "train", 0.9) + val_dataset = TextDataset(text_provider, tokenizer, config.architecture.block_size, "validation", 0.1) cpu_count = os.cpu_count() or 4 train_dataloader = DataLoader( - train_dataset, BATCH_SIZE, pin_memory=True, num_workers=int(cpu_count / 2), shuffle=True + train_dataset, + config.training.batch_size, + pin_memory=True, + num_workers=int(cpu_count / 2), + shuffle=True, ) - val_dataloader = DataLoader(val_dataset, BATCH_SIZE, pin_memory=True, num_workers=int(cpu_count / 2), shuffle=False) + val_dataloader = DataLoader( + val_dataset, + config.training.batch_size, + pin_memory=True, + num_workers=int(cpu_count / 2), + shuffle=False, + ) best_model_path = get_best_model_weights_path(args.experiment) latest_model_path = get_latest_model_weights_path(args.experiment) - model = TransformerLanguageModel(NUM_HEADS, tokenizer.vocab_size, N_EMBED, BLOCK_SIZE, NUM_BLOCKS) - model = load_model(best_model_path, model, DEVICE) + model = TransformerLanguageModel( + config=config, + device=device, + ) + model = load_model(best_model_path, model, device) - print_model_complexity(model) - optimizer = AdamW(model.parameters(), lr=LEARNING_RATE) + print_model_complexity(model, config, device) + optimizer = AdamW(model.parameters(), lr=config.training.learning_rate) best_val_loss = float("inf") @@ -310,19 +167,29 @@ def main() -> None: os.makedirs(args.experiment, exist_ok=True) save_tokenizer(args.experiment, tokenizer) + model_config = f"{args.experiment}/scratch_gpt.yaml" + print(f"Saving this models config to {model_config}") + to_yaml_file(model_config, config) try: - for epoch in range(MAX_EPOCHS): - print(f"Epoch {epoch + 1}/{MAX_EPOCHS}") + for epoch in range(config.training.max_epochs): + print(f"Epoch {epoch + 1}/{config.training.max_epochs}") train_loss_mean, train_loss_std = run_epoch( - model=model, dataloader=train_dataloader, device=DEVICE, stage="train", optimizer=optimizer + model=model, + dataloader=train_dataloader, + device=device, + stage="train", + optimizer=optimizer, ) print(f"Training Loss: {train_loss_mean:.4f} ± {train_loss_std:.4f}") torch.save(model.state_dict(), latest_model_path) val_loss_mean, val_loss_std = run_epoch( - model=model, dataloader=val_dataloader, device=DEVICE, stage="validation" + model=model, + dataloader=val_dataloader, + device=device, + stage="validation", ) print(f"Validation Loss: {val_loss_mean:.4f} ± {val_loss_std:.4f}") @@ -337,7 +204,7 @@ def main() -> None: print("Trying my best here") prompt = input("Tell me your prompt: ") - context = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0).to(DEVICE) + context = torch.tensor(tokenizer.encode(prompt)).unsqueeze(0).to(device) generated = model.generate(context, max_new_tokens=500) first_batch_trained = tokenizer.decode(generated[0].tolist()) print(first_batch_trained) diff --git a/scratchgpt/model/model.py b/scratchgpt/model/model.py new file mode 100644 index 0000000..535d159 --- /dev/null +++ b/scratchgpt/model/model.py @@ -0,0 +1,213 @@ +import math +from typing import Any + +from ptflops import get_model_complexity_info +import torch +from torch import Tensor, nn +from torch.nn import functional as F + +from scratchgpt.config import ScratchGPTConfig + + +class Head(nn.Module): + def __init__( + self, + embedding_size: int, + block_size: int, + head_size: int, + dropout_rate: float, + ) -> None: + super().__init__() + + self._key = nn.Linear(embedding_size, head_size, bias=False) + self._query = nn.Linear(embedding_size, head_size, bias=False) + self._value = nn.Linear(embedding_size, head_size, bias=False) + self._dropout = nn.Dropout(dropout_rate) + self.register_buffer("tril", torch.tril(torch.ones(block_size, block_size))) + + def forward(self, context: Tensor) -> Tensor: + B, T, C = context.shape + key = self._key(context) + query = self._query(context) + + normalization_term: float = 1.0 / math.sqrt(C) + attention_scores = query @ key.transpose(-2, -1) * normalization_term + attention_scores = attention_scores.masked_fill( + self.tril[:T, :T] == 0, # type: ignore + float("-inf"), + ) + attention_scores = F.softmax(attention_scores, dim=-1) + + attention_scores = self._dropout(attention_scores) + + value = self._value(context) + + out: Tensor = attention_scores @ value + return out + + +class MultiHeadAttention(nn.Module): + def __init__( + self, + num_heads: int, + embedding_size: int, + block_size: int, + head_size: int, + dropout_rate: float, + ) -> None: + super().__init__() + self._heads = nn.ModuleList(Head(embedding_size, block_size, head_size, dropout_rate) for _ in range(num_heads)) + self._proj = nn.Linear(embedding_size, embedding_size) + self._dropout = nn.Dropout(dropout_rate) + + def forward(self, context: Tensor) -> Tensor: + out = torch.cat([head(context) for head in self._heads], dim=-1) + out = self._proj(out) + out = self._dropout(out) + return out + + +class FeedFoward(nn.Module): + def __init__(self, embedding_size: int) -> None: + super().__init__() + self._ffwd_multipler = 4 + self._dropout = 0.2 + + self._net = nn.Sequential( + nn.Linear(embedding_size, embedding_size * self._ffwd_multipler), + nn.ReLU(), + nn.Linear(self._ffwd_multipler * embedding_size, embedding_size), + nn.Dropout(self._dropout), + ) + + def forward(self, tensor: Tensor) -> Tensor: + out: Tensor = self._net(tensor) + return out + + +class Block(nn.Module): + def __init__( + self, + num_heads: int, + embedding_size: int, + block_size: int, + dropout_rate: float, + ) -> None: + super().__init__() + head_size = embedding_size // num_heads + self._self_attn_heads = MultiHeadAttention( + num_heads, + embedding_size, + block_size, + head_size, + dropout_rate, + ) + self._ffwd = FeedFoward(embedding_size) + self._layer_norm_attention = nn.LayerNorm(embedding_size) + self._layer_norm_ffwd = nn.LayerNorm(embedding_size) + + def forward(self, tensor: Tensor) -> Tensor: + normal_tensor = self._layer_norm_attention(tensor) + + tensor = tensor + self._self_attn_heads(normal_tensor) + + normal_tensor = self._layer_norm_ffwd(tensor) + tensor = tensor + self._ffwd(normal_tensor) + return tensor + + +class TransformerLanguageModel(nn.Module): + def __init__( + self, + config: ScratchGPTConfig, + device: torch.device, + ) -> None: + super().__init__() + arch = config.architecture + training = config.training + assert arch.vocab_size is not None, "Must supply vocabulary size" + + self._block_size = arch.block_size + self._token_embedding_table = nn.Embedding(arch.vocab_size, arch.embedding_size) + self._position_embedding_table = nn.Embedding( + arch.block_size, + arch.embedding_size, + ) + self._blocks = nn.Sequential( + *[ + Block( + arch.num_heads, + arch.embedding_size, + arch.block_size, + training.dropout_rate, + ) + for _ in range(arch.num_blocks) + ] + ) + self._block_norm = nn.LayerNorm(arch.embedding_size) + self._lm_head = nn.Linear(arch.embedding_size, arch.vocab_size) + self._device = device + + def forward(self, context: Tensor, targets: Tensor | None = None) -> tuple[Tensor, Tensor]: + context = context.long() + B, T = context.shape + + tok_emb = self._token_embedding_table(context) # B, T, C + pos_emb = self._position_embedding_table(torch.arange(T, device=self._device)) # (T, C) + x = tok_emb + pos_emb # B, T, C + x = self._blocks(x) + x = self._block_norm(x) + logits = self._lm_head(x) # (B, T, vocab_size) + + if targets is None: + loss = torch.empty(0) + else: + B, T, C = logits.shape + logits = logits.view(B * T, C) + targets = targets.view(B * T) + loss = F.cross_entropy(logits, targets) + + return logits, loss + + def generate(self, context: Tensor, max_new_tokens: int) -> Tensor: + for _ in range(max_new_tokens): + cropped_context = context[:, -self._block_size :] + logits, _loss = self(cropped_context) + logits = logits[:, -1, :] # becomes (B, C) + probs = F.softmax(logits, dim=-1) + idx_next = torch.multinomial(probs, num_samples=1) + context = torch.cat((context, idx_next), dim=1) + return context + + +def print_model_complexity(model: TransformerLanguageModel, config: ScratchGPTConfig, device: torch.device) -> None: + total_params = sum(p.numel() for p in model.parameters()) + trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + + print("=== MODEL COMPLEXITY ===") + print(f"Total parameters: {total_params:,}") + print(f"Trainable parameters: {trainable_params:,}") + print(f"Model size: {total_params * 4 / 1024 / 1024:.2f} MB (float32)") + + input_shape = (config.architecture.block_size,) + + def input_constructor(input_shape: Any) -> Tensor: + return torch.randint( + 0, + model._token_embedding_table.num_embeddings, + (1,) + input_shape, + device=device, + ) + + flops, params = get_model_complexity_info( + model, + input_shape, + input_constructor=input_constructor, # type: ignore + print_per_layer_stat=False, + as_strings=False, + ) + + print(f" FLOPs per forward pass: {flops:,}") + print(f"GFLOPs per forward pass: {flops / 1e9:.2f}") + + print("=========================") diff --git a/scratchgpt/model_io.py b/scratchgpt/model_io.py index ab72b37..a34c301 100644 --- a/scratchgpt/model_io.py +++ b/scratchgpt/model_io.py @@ -2,7 +2,8 @@ import pickle import torch -from torch import nn + +from scratchgpt.model.model import TransformerLanguageModel from .tokenizer.base_tokenizer import Tokenizer from .tokenizer.tiktoken import TiktokenWrapper @@ -24,7 +25,7 @@ def get_tokenizer_path(exp_folder: str) -> str: return os.path.join(exp_folder, "tokenizer.pkl") -def load_model(model_path: str, model: nn.Module, device: torch.device) -> nn.Module: +def load_model(model_path: str, model: TransformerLanguageModel, device: torch.device) -> TransformerLanguageModel: model.to(device) if os.path.exists(model_path): try: diff 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