From 0802890b39234e802af142da1510008ed8b9fc54 Mon Sep 17 00:00:00 2001 From: henry Date: Mon, 4 Mar 2024 11:22:58 +0000 Subject: [PATCH] added more models --- README.md | 78 +++++++++-- nanodl/__init__.py | 10 +- nanodl/__src/models/reward.py | 247 ++++++++++++++++++++++++++++++++++ nanodl/__src/models/rlhf.py | 0 reward_model_weights.pkl | Bin 0 -> 2870295 bytes tests/test_models.py | 13 ++ 6 files changed, 335 insertions(+), 13 deletions(-) create mode 100644 nanodl/__src/models/reward.py delete mode 100644 nanodl/__src/models/rlhf.py create mode 100644 reward_model_weights.pkl diff --git a/README.md b/README.md index 14e47d4..d74c6d5 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ Developing and training transformer-based models is typically resource-intensive - A wide array of blocks and layers, facilitating the creation of customised transformer models from scratch. - An extensive selection of models like Gemma, LlaMa2, Mistral, Mixtral, GPT3, GPT4 (inferred), T5, Whisper, ViT, Mixers, GAT, CLIP, and more, catering to a variety of tasks and applications. -- Data-parallel distributed trainers includding RLHFPPO and RLHFDPO so developers can efficiently train large-scale models on multiple GPUs or TPUs, without the need for manual training loops. +- Data-parallel distributed trainers includding RLHF so developers can efficiently train large-scale models on multiple GPUs or TPUs, without the need for manual training loops. - Dataloaders, making the process of data handling for Jax/Flax more straightforward and effective. - Custom layers not found in Flax/Jax, such as RoPE, GQA, MQA, and SWin attention, allowing for more flexible model development. - GPU/TPU-accelerated classical ML models like PCA, KMeans, Regression, Gaussian Processes etc., akin to SciKit Learn on GPU. @@ -35,7 +35,7 @@ Feedback on any of our discussion, issue and pull request threads are welcomed! ## What's New in version 1.2.0.dev1 - Google's Gemma architecture. -- Data parallel distributed RLHFPPO and RLHFDPO. +- Data parallel distributed Reward and RLHF trainers. - True random number generators in Jax which do not need the verbose code (examples shown in next sections). There are experimental features (like MAMBA architecture) in the repo which is not available via the package, @@ -62,7 +62,7 @@ pip install nanodl ## What does nanodl look like? -We provide various examples using the nanodl API: language, vision and audio, starting with an LLM. +We provide various example usages of the nanodl API. ```py import jax @@ -139,7 +139,9 @@ outputs = model.apply({'params': params}, method=model.generate) print(outputs) ``` + Vision example + ```py import jax import jax.numpy as jnp @@ -186,6 +188,7 @@ print(generated_images.shape) ``` Audio example + ```py import jax import jax.numpy as jnp @@ -210,12 +213,6 @@ dataloader = DataLoader(dataset, shuffle=True, drop_last=False) -# How to loop through dataloader -for batch in dataloader: - x, y = batch - print(x.shape, y.shape) - break - # model parameters hyperparams = { 'num_layers': 1, @@ -256,7 +253,67 @@ transcripts = model.apply({'params': params}, print(transcripts) ``` +Reward Model example for RLHF + +```py +import jax +import jax.numpy as jnp +from nanodl import ArrayDataset, DataLoader +from nanodl import Mistral, RewardModel, RewardDataParallelTrainer + +# Generate dummy data +batch_size = 8 +max_length = 10 + +# Replace with actual tokenised data +dummy_chosen = jnp.ones((101, max_length), dtype=jnp.int32) +dummy_rejected = jnp.zeros((101, max_length), dtype=jnp.int32) + +# Create dataset and dataloader +dataset = ArrayDataset(dummy_chosen, dummy_rejected) +dataloader = DataLoader(dataset, + batch_size=batch_size, + shuffle=True, + drop_last=False) + + # model parameters +hyperparams = { + 'num_layers': 1, + 'hidden_dim': 256, + 'num_heads': 2, + 'feedforward_dim': 256, + 'dropout': 0.1, + 'vocab_size': 1000, + 'embed_dim': 256, + 'max_length': max_length, + 'start_token': 0, + 'end_token': 50, + 'num_groups': 2, + 'window_size': 5, + 'shift_size': 2 +} + +# Initialize reward model from Mistral +model = Mistral(**hyperparams) +reward_model = RewardModel(model, dim=hyperparams['hidden_dim'], dropout=0.1) + +# Train the reward model +trainer = RewardDataParallelTrainer(reward_model, dummy_chosen.shape, 'reward_model_weights.pkl') +trainer.train(dataloader, 5, dataloader) +params = trainer.load_params('reward_model_weights.pkl') + +# Call as you would a regular Flax model +rngs = jax.random.PRNGKey(0) +rngs, dropout_rng = jax.random.split(rngs) +rewards = reward_model.apply({'params': params}, + dummy_chosen, + rngs={'dropout': dropout_rng}) + +print(rewards.shape) +``` + PCA example + ```py import jax from nanodl import PCA @@ -272,6 +329,7 @@ print(X_sampled.shape, original_data.shape, transformed_data.shape) NanoDL provides random module which abstracts away Jax's intricacies. It generates truly random variables by using the current timestamp as seed. + ```py # Jax example key = random.PRNGKey(0) @@ -289,7 +347,7 @@ This is the first iteration of this project, roughness is expected, contribution - Raise the issue/discussion to get second opinions - Fork the repository - Create a branch -- Make your changes without ruining the design patterns +- Make your changes without changing the design patterns - Write tests for your changes if necessary - Install locally with `pip install -e .` - Run tests with `python -m unittest discover -s tests` diff --git a/nanodl/__init__.py b/nanodl/__init__.py index 51ae548..d5c9586 100644 --- a/nanodl/__init__.py +++ b/nanodl/__init__.py @@ -127,6 +127,11 @@ GemmaDecoderBlock ) +from nanodl.__src.models.reward import ( + RewardModel, + RewardDataParallelTrainer +) + from nanodl.__src.utils.data import ( Dataset, ArrayDataset, @@ -232,13 +237,12 @@ "Mixtral", "MixtralDecoder", "MixtralDecoderBlock", - "Mamba", - "MambaDataParallelTrainer", - "MambaBlock" "Whisper", "WhisperDataParallelTrainer", "WhisperSpeechEncoder", "WhisperSpeechEncoderBlock", + "RewardModel", + "RewardDataParallelTrainer", "DiffusionModel", "DiffusionDataParallelTrainer", "UNet", diff --git a/nanodl/__src/models/reward.py b/nanodl/__src/models/reward.py new file mode 100644 index 0000000..828499a --- /dev/null +++ b/nanodl/__src/models/reward.py @@ -0,0 +1,247 @@ +import jax +import flax +import time +import optax +import jax.numpy as jnp +import flax.linen as nn +from flax.training import train_state +from typing import Tuple, Any, Optional, Iterable + + +class RewardModel(nn.Module): + """ + The RewardModel estimates the reward or value of a given input sequence, + typically used in reinforcement learning frameworks for natural language processing tasks. + It uses the last hidden state of a transformer-based model to generate a scalar reward prediction, + guiding the agent's behavior by evaluating the desirability or utility of its generated outputs. + + Example: + ```python + from nanodl import ArrayDataset, DataLoader + from nanodl import Gemma, RewardModel, RewardDataParallelTrainer + + # Generate dummy data + batch_size = 8 + max_length = 10 + + # Replace with actual tokenised data + dummy_chosen = jnp.ones((101, max_length), dtype=jnp.int32) + dummy_rejected = jnp.zeros((101, max_length), dtype=jnp.int32) + + # Create dataset and dataloader + dataset = ArrayDataset(dummy_chosen, dummy_rejected) + dataloader = DataLoader(dataset, + batch_size=batch_size, + shuffle=True, + drop_last=False) + + # model parameters + hyperparams = { + 'num_layers': 1, + 'hidden_dim': 256, + 'num_heads': 2, + 'feedforward_dim': 256, + 'dropout': 0.1, + 'vocab_size': 1000, + 'embed_dim': 256, + 'max_length': max_length, + 'start_token': 0, + 'end_token': 50, + 'num_groups': 2, + } + + # Initialize reward model from Gemma + model = Gemma(**hyperparams) + reward_model = RewardModel(model, dim=hyperparams['hidden_dim'], dropout=0.1) + + # Train the reward model + trainer = RewardDataParallelTrainer(reward_model, dummy_chosen.shape, 'reward_model_weights.pkl') + trainer.train(dataloader, 5, dataloader) + params = trainer.load_params('reward_model_weights.pkl') + + # Call as you would a regular Flax model + rngs = jax.random.PRNGKey(0) + rngs, dropout_rng = jax.random.split(rngs) + rewards = reward_model.apply({'params': params}, + dummy_chosen, + rngs={'dropout': dropout_rng}) + + print(rewards.shape) + ``` + """ + model: nn.Module + dim: int + dropout: float + + @nn.compact + def __call__(self, + x: jnp.ndarray, + training: bool = False): + + x = self.model(x, training=training, drop_last_layer=True) + x = nn.Dropout(rate=self.dropout)(x, deterministic=not training) + x = nn.Dense(1)(x[:, -1:, 0]) + return nn.sigmoid(x) + + +class RewardDataParallelTrainer: + """ + Trainer class using data parallelism with JAX. + This trainer leverages JAX's `pmap` for parallel training across multiple devices (GPUs/TPUs). + It handles the model training loop, including gradient computation, parameter updates, and evaluation. + + Attributes: + model (Any): The model to be trained. + input_shape (Tuple[int, ...]): The shape of the input tensor. + weights_filename (str): Filename where the trained model weights will be saved. + learning_rate (float): Learning rate for the optimizer. + params_path (Optional[str]): Path to pre-trained reward model parameters for initializing the REWARD model, if available. + model_params_path (Optional[str]): Path to pre-trained backbone model parameters for initializing the BACKBONE model, if available. + + Methods: + create_train_state(learning_rate, text_input_shape, image_input_shape): Initializes the training state, including parameters and optimizer. + train_step(state, texts, images): Performs a single training step, including forward pass, loss computation, and gradients update. + train(train_loader, num_epochs, val_loader): Runs the training loop over the specified number of epochs, using the provided data loaders for training and validation. + evaluation_step(state, texts, images): Performs an evaluation step, computing forward pass and loss without updating model parameters. + evaluate(test_loader): Evaluates the model performance on a test dataset. + save_params(): Saves the model parameters to a file. + load_params(filename): Loads model parameters from a file. + """ + def __init__(self, + model: Any, + input_shape: Tuple[int, ...], + weights_filename: str, + learning_rate: float = 1e-5, + params_path: Optional[str] = None, + model_params_path: Optional[str] = None) -> None: + + self.model = model + self.params = None + self.params_path = params_path + self.model_params_path = model_params_path + self.num_parameters = None + self.best_val_loss = float("inf") + self.weights_filename = weights_filename + self.num_devices = jax.local_device_count() + self.train_step = jax.pmap(RewardDataParallelTrainer.train_step, axis_name='devices') + self.evaluation_step = jax.pmap(RewardDataParallelTrainer.evaluation_step, axis_name='devices') + self.state = self.create_train_state(learning_rate, input_shape) + print(f'Number of accelerators: {self.num_devices}') + + + def create_train_state(self, + learning_rate: float, + input_shape: Tuple[int, ...]) -> Any: + + rngs = {'params': jax.random.key(0), 'dropout': jax.random.key(1)} + params = self.model.init(rngs, + jnp.ones(input_shape, dtype=jnp.int32))['params'] + + if self.params_path is not None: + params = self.load_params(self.params_path) + + if self.model_params_path is not None: + model_params = self.load_params(self.model_params_path) + params = self.merge_params(model_params, params) + + self.num_parameters = sum(param.size for param in jax.tree_util.tree_leaves(params)) + print(f'Number of parameters: {self.num_parameters}') + state = train_state.TrainState.create(apply_fn=self.model.apply, + params=params, + tx=optax.adam(learning_rate)) + return jax.device_put_replicated(state, jax.local_devices()) + + @staticmethod + def train_step(state: Any, + chosen: jnp.ndarray, + rejected: jnp.ndarray) -> Tuple[Any, jnp.ndarray]: + + def loss_fn(params): + chosen_rewards = state.apply_fn({'params': params}, + chosen, + training=True, + rngs={'dropout': jax.random.PRNGKey(int(time.time()))}) + + rejected_rewards = state.apply_fn({'params': params}, + rejected, + training=True, + rngs={'dropout': jax.random.PRNGKey(int(time.time()))}) + + return -jnp.log(jax.nn.sigmoid(chosen_rewards - rejected_rewards)).mean() + + loss, grads = jax.value_and_grad(loss_fn)(state.params) + state = state.apply_gradients(grads=grads) + return state, loss + + def train(self, + train_loader: Iterable[Tuple[jnp.ndarray, jnp.ndarray]], + num_epochs: int, + val_loader: Optional[Iterable[Tuple[jnp.ndarray, jnp.ndarray]]] = None) -> None: + + for epoch in range(num_epochs): + total_loss = 0.0 + count = 0 + for chosen, rejected in train_loader: + batch_size = chosen.shape[0] + batch_size_per_device = batch_size // self.num_devices + chosen = chosen.reshape((self.num_devices, batch_size_per_device, -1)) + rejected = rejected.reshape((self.num_devices, batch_size_per_device, -1)) + self.state, loss = self.train_step(state=self.state, + chosen=chosen, + rejected=rejected) + total_loss += jnp.mean(loss) + count += 1 + + mean_loss = total_loss / count + print(f'Epoch {epoch+1}, Train Loss: {mean_loss}') + + if val_loader is not None: + val_loss = self.evaluate(val_loader) + print(f'Epoch {epoch+1}, Val Loss: {val_loss}') + if val_loss < self.best_val_loss: + self.best_val_loss = val_loss + print("New best validation score achieved, saving model...") + self.save_params() + return + + @staticmethod + def evaluation_step(state: Any, + chosen: jnp.ndarray, + rejected: jnp.ndarray) -> Tuple[Any, jnp.ndarray]: + chosen_rewards = state.apply_fn({'params': state.params}, chosen, rngs={'dropout': jax.random.PRNGKey(2)}) + rejected_rewards = state.apply_fn({'params': state.params}, rejected, rngs={'dropout': jax.random.PRNGKey(2)}) + return -jnp.log(jax.nn.sigmoid(chosen_rewards - rejected_rewards)).mean() + + def evaluate(self, + test_loader: Iterable[Tuple[jnp.ndarray, jnp.ndarray]]) -> None: + + total_loss = 0.0 + count = 0 + for chosen, rejected in test_loader: + batch_size = chosen.shape[0] + batch_size_per_device = batch_size // self.num_devices + chosen = chosen.reshape((self.num_devices, batch_size_per_device, -1)) + rejected = rejected.reshape((self.num_devices, batch_size_per_device, -1)) + loss = self.evaluation_step(self.state, chosen, rejected) + total_loss += jnp.mean(loss) + count += 1 + + mean_loss = total_loss / count + return mean_loss + + def merge_params(untrained_params, trained_params): + updated_untrained_params = jax.tree_map( + lambda untrained, trained: trained if untrained.shape == trained.shape else untrained, + untrained_params, + trained_params) + return updated_untrained_params + + def save_params(self) -> None: + self.params = flax.jax_utils.unreplicate(self.state.params) + with open(self.weights_filename, 'wb') as f: + f.write(flax.serialization.to_bytes(self.params)) + + def load_params(self, filename: str): + with open(filename, 'rb') as f: + self.params = flax.serialization.from_bytes(self.params, f.read()) + return self.params \ No newline at end of file diff --git a/nanodl/__src/models/rlhf.py b/nanodl/__src/models/rlhf.py deleted file mode 100644 index e69de29..0000000 diff --git a/reward_model_weights.pkl b/reward_model_weights.pkl new file mode 100644 index 0000000000000000000000000000000000000000..d16069d1733bb83ef953c608d79c3e4a122306f6 GIT binary patch literal 2870295 zcmYhCc~nhr*!~+dPnwg`Kt-iNH0|d;Ql>;GC1hwO8c-3UISraiX%eAAG#T2@eP~jm zB2tu0NtrS?@a66I$8Wvwe|zn-_E~G6z0Y%B*XO#P$O}uo{P%lpH;OEA-{rQyMVdDw zjrW50o&dKXlLbxFnO<n_aqjpC zT>Rq#kNanX+qd1|`OyfP-(J8)-{sL}dJcR}y@N0HRq*idTxbZdXMAKU$g$#VA^_F}?1xh9sqqL)K?~v?-tmW^~~AqaJ#$=QMMsREiiheWNqaOv4O6QGEL$4s10KfX;${ 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