intro: Let’s reproduce GPT-2 (124M)
* *** Exploring the Target:* The video starts by loading the pre-trained GPT-2 (124M) model from Hugging Face Transformers and examining its weights and architecture.
- 🤖 Reproducing GPT-2 124M model- Reproducing the GPT-2 model involves understanding its release structure and model variations.
M) model, including its state dictionary and tensor shapes. We learn how the model's vocabulary size and embedding dimensions are represented within these tensors. (-
Reproducing the GPT-2 124M version
- 💻 Model Parameters Overview- GPT-2 miniseries comprises models of various sizes, with the 124 million parameter model being a significant variant.- Model parameters dictate its size, layer count, and channel dimensions, affecting downstream task performance.
- 💰 Reproducibility and Cost- Reproducing the GPT-2 124M model is now more accessible and affordable due to advances in hardware and cloud computing.- Achieving comparable model performance can be done in a relatively short time and at a reasonable cost.
Validation loss measures model's performance on unseen data.
- 📚 Reference Material- Access to GPT-2 weights facilitates reproduction, but additional references like the GPT-3 paper provide crucial details for optimization and training settings.- Combining insights from both GPT-2 and GPT-3 papers enhances reproducibility and understanding of the model architecture.
exploring the GPT-2 (124M) OpenAI checkpoint
@ now... so far so good...
compared to the original Transformer are explored, such as the removal of the encoder and cross-attention mechanism. Further, modifications to layer normalization placement and the addition of a final layer normalization layer are highlighted. (-
- 🧠 Understanding Model Structure- Exploring the structure of the GPT-2 model involves inspecting token and positional embeddings, as well as layer weights.- The visualization of embeddings and weights reveals insights into the model's learning process and representation.
GPT-2 token and position embeddings explained
, aligning it with the schema used by Hugging Face Transformers. This skeleton includes modules for token and positional embeddings, Transformer blocks, final layer normalization, and the language model head. (-
Understanding token positions and embeddings in GPT-2 (124M)
has the freedom to learn the position embeddings (the original transformer paper hardcoded the positional embeddings)
is discussed. (-
Implementing and understanding GPT-2 (124M) model architecture.
- 🛠 Implementing Model Architecture- Developing a custom GPT-2 model involves constructing the model architecture, including token and position embeddings, transformer blocks, and classification layers.- Aligning the custom implementation with existing frameworks like Hugging Face Transformers aids in loading pre-trained weights and ensures compatibility.
SECTION 1: implementing the GPT-2 nn.Module
* *** Implementing the GPT-2 nn.Module:* A custom GPT-2 class is built in PyTorch, mirroring the Hugging Face architecture and loading the pre-trained weights for verification.
- 🔍 Model Architecture Differences- GPT-2's architecture includes modifications like layer normalization adjustments and additional layer normalization in the final self-attention block compared to the original Transformer.- Understanding architectural differences is crucial for accurately implementing and reproducing the GPT-2 model.
Creating a matching schema for loading weights easily.
- 🏗 Defining Model Blocks- Designing the transformer block involves structuring the forward pass, incorporating attention mechanisms, feedforward networks, and residual connections.- Optimizing the block structure for efficient information flow and gradient propagation is essential for model performance.
's implementation through tensor manipulation and its algorithmic similarity to previous implementations. (-
You want a direct residual connection from the target to the input embeddings, skipping layer normalization (I need to understand what layer normalization is)
Found this video first, then at about when you started talking about residuals and micrograd, went back to your zero-to-hero series and watched everything as a prerequisite. now i understand how residuals helps in stabilizing the training. the gradient distribution into branches analogy really changed the perspective for me. this video should be kept safe in a time capsule
The Transformer involves repeated application of map and reduce
Implementing the Forward Pass and Text Generation: The forward pass of the network is implemented, outlining how input token indices are processed to produce logits for predicting the next token in a sequence. This sets the stage for generating text from the model. (-
Its funny how his description of attention as reduce-map description at can be thought of as map-reduce :)
the comparison between attention and mlp is impressive
- 🧠 Understanding the Transformer Architecture- The Transformer architecture relies on attention mechanisms and multi-layer perceptrons (MLPs).- Attention is crucial for communication and individual information processing within Transformer blocks.- Transformers utilize repeated application of "map" and "reduce" operations for information exchange and refinement.
- 🛠 Implementing the MLP Block- The MLP block consists of linear projections sandwiched between G nonlinearity.- The G nonlinearity resembles a smoother version of ReLU and contributes to better gradient flow.- Historical reasons and empirical evidence support the use of the approximate G nonlinearity in GPT-2 reproduction.
GPT-2 used the 10h approximate version of G instead of the exact version.
Activation function GELU is an approximation
- 🧩 Exploring the Attention Operation- Multi-headed attention in Transformers involves parallel computation of attention heads.- The attention operation remains algorithmically equivalent to previous implementations but is more efficient in PyTorch.- Careful variable naming facilitates seamless weight transfer from existing models during reproduction.
model. This involves tokenizing a prefix string, moving the model to a CUDA device for GPU acceleration, and performing sampling-based text generation. (-
GPT-2 (124M) implementation details
Efficient implementation in PyTorch for GPT-2 (124M) model
Introducing the Tiny Shakespeare Dataset: This part introduces the Tiny Shakespeare dataset as a small and manageable dataset for initial model training and debugging. Basic statistics of the dataset are explored. (-
loading the huggingface/GPT-2 parameters
This series is amazing, but I have a bit of confusion. At the timestamp, you mentioned that the weights are transposed and referenced something about TensorFlow. However, I think in PyTorch, the weights for a linear layer are initialized as torch.empty(out_features, in_features)so is this the case u needed to transpose the weightsand Furthermore, the weights you are transposing all belong to linear layers, yet for the last lm_head layer, which is also a linear layer, you are not transposing that weight.Am I mistaken here, or is there something else going on?
Forwarding the GPT-2 model requires processing token indices and embeddings.
implementing the forward pass to get logits
* *** Forward Pass and Sampling:* The forward pass is implemented to calculate logits, and a sampling loop is added to generate text from the model.
model. It introduces the concept of batching and creating input-target pairs for loss calculation. (-
Explaining the forward pass of the GPT-2 network
sampling init, prefix tokens, tokenization
Creating a Simple Data Loader: This section refactors the code to create a simple data loader object responsible for loading tokenized data from the Tiny Shakespeare dataset and generating batches suitable for training the model. (-
Generating logits and probabilities for token prediction
sampling loop
(time )
why do we only keep the last column of the logits?
Using top K by default (50) helps keep the model on track
Calculating Loss and Backpropagation: The forward function is adjusted to return not just the logits but also the calculated loss based on provided target tokens. Cross-entropy loss is used, and the initial loss is sanity-checked to ensure reasonable starting probabilities. (-
- 🤖 Replicating GPT-2 Model Initialization- Replicating the GPT-2 model initialization process.- Transitioning from pre-trained weights to initializing from random numbers.- Exploring the straightforward process of using a random model in PyTorch.
sample, auto-detect the device
: My quick summary at ! A 2000-line GPT-2 implementation in Huggingface has been condensed to almost 100 lines. The weights from HF GPT-2 were replicated in this new version, using the same sampling parameters, seed, and generating identical output. A notable improvement is the restructuring of the implementation, where all heads are now integrated within a single matrix, applying some neat matrix transposes while maintaining parallelism and enhancing comprehension. This is far easier to understand compared to many other complicated multihead implementations I've seen. The next step involves training this model from the ground up.
Using GPT-2 (124M) for model initialization
- 🔍 Detecting and Utilizing Device in PyTorch- Automatically detecting and utilizing available devices in PyTorch.- Strategies for choosing the highest compute-capable device.- Facilitating code compatibility across different hardware configurations.
Implementing Optimization with AdamW: This section introduces the AdamW optimizer as an alternative to stochastic gradient descent (SGD), highlighting its advantages for language model training. The optimization loop is implemented, including gradient accumulation and loss printing. (-
Initializing model on correct device is crucial for performance
let’s train: data batches (B,T) → logits (B,T,C)
- 📄 Preparing and Tokenizing Dataset- Introduction to the Tiny Shakespeare dataset for training.- Obtaining and processing the dataset for tokenization.- Initial exploration and preprocessing steps for training data.
Understanding and Addressing Device Mismatches: This part emphasizes the importance of ensuring all tensors and model components reside on the same device (CPU or GPU) to avoid errors during training. A bug related to tensor device mismatch is identified and corrected. (-
Transforming single sequence into batch with structured tokens
Creating input and labels for Transformer
model based on the original paper's guidelines. This includes using specific standard deviations for different layer types and scaling residual connections to control activation growth. (-
-- 🛠 Implementing Data Loader and Loss Calculation- Building a data loader to feed token sequences into the Transformer model.- Setting up the forward pass to calculate the loss function.- Establishing a structured approach for loss calculation and gradient updates.
cross entropy loss
Flattening multi-dimensional tensors for cross entropy calculation.
Calculating the estimated loss at initialization
GPU, focusing on its theoretical performance limits in terms of Teraflops for different floating-point precisions. The importance of memory bandwidth limitations is also discussed. (-
The loss at initialization is expected to be around 10.82 but is seen around 11, which suggests a diffused probability distribution at initialization.
Fun Fact: -ln(1/50257) = 10.82 but simply ln(50257) also gives the same answer.
optimization loop: overfit a single batch
Question regarding overfitting a single batch .
- 🧮 Optimizing Model Parameters with AdamW- Implementing optimization using the AdamW optimizer.- Understanding the role and benefits of AdamW compared to SGD.- Executing gradient updates and monitoring loss during the optimization process.
Pytorch library had bugs that the canonical version (e.g. Adam) is the buggy version (fixed in AdamW)
, are introduced as ways to trade precision for significant speed improvements. (-
Explaining the device issue and fixing tensor moving bug.
- 🧠 Introduction to Model Optimization- Optimizing model training requires careful handling of tensors and device placement.- Overfitting a single batch is an initial step in understanding model behavior.- Transitioning from overfitting a single batch to optimizing with multiple batches requires implementing a data loader.
Attempting to overfit on a single example
Creating a simple data loader for iterating through batches of data.
data loader lite
- 📊 Implementation of a Simple Data Loader- The data loader reads text files and tokenizes them for model input.- It divides the data into batches, ensuring smooth iteration over the dataset.- Basic functionality covers chunking data and managing batch transitions.
I see at for the batch processing, you are marching along by an index of `B * T`. Instead, what would be the implications of changing this to a sliding window (+1 indexing) such that we get overlapping samples? I realise this would create `len(self.tokens) - block_size` samples leading to a far greater number of batches per epoch, is this the only aspect?
precision in PyTorch to leverage tensor cores and achieve a substantial speedup in training without noticeable accuracy degradation. (-
Bug in GPT-2 training process
parameter sharing wte and lm_head
- 🐛 Fixing a Weight Initialization Bug- Identifies a bug in weight initialization concerning weight tying in GPT-2 training.- Explains the significance of weight tying in reducing parameters and improving performance.- Implements a fix by redirecting pointers to the same tensor, saving parameters and optimizing performance.
Common weight tying scheme in Transformer models
Further Optimization with Torch Compile and Kernel Fusion: The torch.compile function is introduced as a powerful optimization technique that can analyze and fuse multiple operations into single kernels, reducing memory bandwidth bottlenecks and increasing throughput. (-
source code (at ) but I can't seem to find it in your PyTorch implementation.
the weights sharing the dimensions of wte and lm head are different, is it okay?
Weight sharing scheme reduces parameters and improves efficiency
Identifying Performance Bottlenecks: "Nice" vs. "Ugly" Numbers: This section highlights a less obvious optimization technique: ensuring that key parameters like vocabulary size and batch size are "nice" numbers with many powers of two. This helps align computations with CUDA's block-based execution model and avoids inefficient boundary cases. (-
% of the parameters)
- 🎚 Fine-tuning Model Initialization- Discusses the importance of model weight initialization in training stability and performance.- Mimics GPT-2 initialization scheme based on observed patterns in released source code.- Introduces a scaling factor for residual layers' weights initialization to control activation growth in the network.
Follow GPT-2 initialization scheme for better model performance
model initialization: std 0.02, residual init
: Summary at
Adjusting Vocabulary Size for Optimal Performance: This part demonstrates how a slight increase in vocabulary size to the nearest power of two can surprisingly lead to a performance boost due to more efficient CUDA kernel execution. (-
shouldn't Embedding std be set to 0.01 ?
Controlling growth of activations in the residual stream
Setting flags and scaling standard deviation in GPT-2 model initialization.
Implementing Gradient Accumulation for Large Batch Sizes: This section introduces gradient accumulation as a technique to simulate very large batch sizes that wouldn't fit in GPU memory by accumulating gradients over multiple micro-batches before performing a weight update. (-
Hi Andrej should we skip the pos embedding initialization with std 0.01 like in the original code and stick to the 0.02 ?
- 🛠 Implementing GPT-2 Initialization- Implementing scaling down the standard deviation for proper initialization.- Clarification on the two times number of layers in the Transformer.- Setting seeds for reproducibility and initializing GPT-2 model.
SECTION 2: Let’s make it fast. GPUs, mixed precision, 1000ms
* *** Understanding Hardware:* The video emphasizes understanding GPU capabilities, particularly tensor cores and memory bandwidth.
- 💻 Optimizing Hardware Utilization- Assessing available hardware resources, including GPUs.- Understanding the importance of memory bandwidth in GPU utilization.- Exploring precision options (float32, tf32, bfloat16) for performance optimization.
import code; code.interact(local=locals())
Deep learning training can achieve higher performance by using lower precision formats.
Utilizing Multiple GPUs with Distributed Data Parallelism: This part introduces the concept of distributed data parallelism (DDP) to utilize multiple GPUs for training. It explains how to launch multiple processes with torchrun, assign processes to specific GPUs, and synchronize gradients across processes. (-
Importance of using floating points over int8 for neural network training.
model. The data loading script and its functionalities for downloading, tokenizing, and sharding the dataset are briefly explained. (-
- 🔄 Leveraging Tensor Cores for Acceleration- Explanation of tensor cores and their role in matrix multiplication.- Introduction to tf32 precision and its performance benefits.- Comparison of tf32 and float32 performance improvements.
Tensor Cores, timing the code, TF32 precision, 333ms
* *** Mixed Precision (TF32):* Enabling TF32 precision for matrix multiplications provides a free 3x speedup with minimal accuracy loss.
Matrix multiplication is accelerated through tensor cores.
Adjusting Training Script for Fine Web EDU: The training script is modified to accommodate the Fine Web EDU dataset, including changes to the data loader, training loop, and hyperparameter settings. The concept of warming up the learning rate and its importance in training large language models is discussed. (-
Using tf32 for 8X faster performance with minor precision tradeoff.
Max out the batch size and use numbers with powers of two for better efficiency.
model on HSWAG are outlined. (-
@ Should the tokens/second throughput be x2 given we use both X and y (targets) for training? Or are we just looking at the batch size here? Also would using x.numel() or y.numel() be equivalent?
- ⚙ Implementing tf32 Precision in PyTorch- Enabling tf32 precision in PyTorch with a single line of code.- Observing throughput improvements with tf32 precision.- Understanding the trade-offs and limitations of tf32 precision.
TF32 promises 8X throughput but only delivers 3X due to memory bottlenecks
float16, gradient scalers, bfloat16, 300ms
* *** Mixed Precision (BFloat16):* Switching to BFloat16 for activations further improves speed, requiring minimal code changes thanks to PyTorch AutoCast.
model. The importance of a validation set in monitoring overfitting is reiterated. (-
- 📊 B Float16 vs. FP16 Precision Reduction- Understanding B Float16 precision reduction compared to FP16.- B Float16 maintains the same exponent range but truncates the mantissa, resulting in reduced precision within the range.- Unlike FP16, B Float16 does not alter the range of representable numbers, simplifying training processes by eliminating the need for gradient scalers.
Transition from fp16 to bf16 for simpler training.
- 🧮 Implementing Mixed Precision in PyTorch- Utilizing PyTorch's torch.AutoCast for mixed precision training.- Guidance on using torch.AutoCast to surround the forward pass and loss calculation in the model.- Highlighting the minimal code changes required to implement B Float16 training in PyTorch.
Implementing B float 16 for minimal impact on model activations.
training for further performance optimization. (-
)
Introducing torch.compile for faster model compilation
torch.compile, Python overhead, kernel fusion, 130ms
* *** Torch.Compile:* Compiling the model with torch.compile significantly reduces Python overhead and optimizes kernel fusion, resulting in a 2.3x speedup.
- ⚡ Torch.Compile for Model Optimization- Introduction to torch.Compile as a compiler for neural networks in PyTorch.- Explaining the reduction of Python overhead and GPU read-writes for faster computation.- Demonstrating significant speed improvements with torch.Compile, achieving about 2.3x faster performance with a single line of code.
Torch compile optimizes neural net operations efficiently
"dispatch the kernel"???
Optimizing round trips to GPU memory for faster computation
GPU chip architecture overview
hours of video on this topic"Me: Please sign me up :)
yes Andrej we need that 2 hour neural net Hardware specific video 🗣🗣🗣
Torch compilation utilizes kernel Fusion for speed optimization
flash attention, 96ms
* *** Flash Attention:* Replacing the default attention implementation with Flash Attention, a specialized kernel fusion algorithm, yields another 27% speedup.
- 🧠 Flash Attention Optimization- Flash attention is a kernel fusion algorithm that significantly speeds up attention mechanisms.- Achieves faster computation by avoiding materializing large attention matrices.- Utilizes an online softmax trick to incrementally evaluate softmax without storing all inputs.
Flash attention algorithm reduces memory usage and improves computation speed significantly.
FlashAttention -> more flops does not mean slower
Using Flash attention in PyTorch for faster runtime.
nice/ugly numbers. vocab size 50257 → 50304, 93ms
* *** Nice vs. Ugly Numbers:* Optimizing vocabulary size to a power of two (50304) for better kernel utilization surprisingly provides a 4% speedup.
- 🧮 Optimization with Nice Numbers- Identifies "nice" numbers (powers of two) as optimal for computations in CUDA.- Adjusts vocabulary size to a nice number to improve computation efficiency.- Padding inputs to align with block sizes in CUDA can lead to significant performance gains.
Prefer using powers of two in code for neural networks and CUDA.
Add more tokens the model actually trains faster
Improved GPT-2 performance by fixing token index issue
Padding inputs for efficiency improvement
SECTION 3: hyperpamaters, AdamW, gradient clipping
* *** Hyperparameters and AdamW:* The video adopts hyperparameters from the GPT-3 paper, including AdamW optimizer settings and gradient clipping.
- 🔍 Hyperparameter Tuning and Algorithmic Improvements- Discusses the importance of hyperparameter tuning based on the GPT-3 paper.- Implements gradient norm clipping to prevent model instability during optimization.- Monitoring the gradient norm helps detect training instabilities and adjust optimization strategies.
Setting hyperparameters for training GPT-3
Monitoring gradient norm is crucial for stability
- 🎓 Implementing Learning Rate Scheduler and Weight Decay- Understanding the details of the learning rate scheduler and weight decay implementation:- Learning rate scheduler: Cosine decay with warm-up period and decay to 10% over a specified horizon.- Weight decay: Used for regularization, typically applied to embedding and weight matrices.
learning rate scheduler: warmup + cosine decay
* *** Learning Rate Scheduler:* A cosine decay learning rate schedule with warmup is implemented, following the GPT-3 paper.
Setting learning rate in GPT-2 (124M)
Implementing a learning rate schedule for training GPT-2
batch size schedule, weight decay, FusedAdamW, 90ms
* *** Batch Size, Weight Decay, FusedAdamW:* The video discusses batch size scheduling (which is ultimately skipped), implements weight decay for regularization, and utilizes the fused implementation of AdamW for further speed improvements.
- 📊 Batch Size Increase and Data Sampling Techniques- Explanation on gradual batch size increase and data sampling methods:- Gradual batch size increase: Linear ramp-up from small to large batch sizes, aiming for system speed improvement.- Data sampling without replacement: Exhausting a pool of data without reusing sequences until an epoch boundary is reached.
Data are sampled without replacement during training.
- 🧮 Weight Decay Implementation and Optimizer Configuration- Details on weight decay implementation and optimizer configuration:- Weight decay: Applied for regularization, particularly to embeddings and weight matrices.- Optimizer configuration: Adjusting parameters for optimal training performance, including weight decay settings.
Weight decay parameters are split into those that should be weight decayed and those that should not be weight decayed.
Weight decay is applied to two-dimensional parameters.
gradient accumulation
* *** Gradient Accumulation:* Gradient accumulation is implemented to simulate larger batch sizes (0.5 million tokens) on limited GPU memory.
Using gradient accumulation to simulate a large batch size
- 🔄 Gradient Accumulation for Simulating Large Batch Sizes- Implementation of gradient accumulation technique to simulate large batch sizes:- Total batch size setting: Defines the desired batch size, which may exceed GPU capacity.- Micro batch size and gradient accumulation: Processing multiple micro-batches and accumulating gradients before updating the model.
- 🧠 Understanding Gradient Accumulation- Explains the concept of gradient accumulation.- Demonstrates the difference between traditional batch processing and gradient accumulation.- Emphasizes the importance of normalizing gradients to ensure consistency.
Demonstration of simple neural network implementation with mean squared loss
Gradients do not match due to loss normalization issue
Optimizing model training with gradient accumulation and distributed data parallelism.
distributed data parallel (DDP)
* *** Distributed Data Parallel (DDP):* The training is parallelized across 8 GPUs using PyTorch DDP, achieving a throughput of 1.5 million tokens per second.
- 🔧 Implementing Distributed Data Parallelism- Introduces the concept of distributed data parallelism for utilizing multiple GPUs.- Explains the difference between legacy data parallelism and distributed data parallelism.- Describes how distributed data parallelism works and its benefits in training neural networks.
Collaborative processing with multiple GPUs
Running with TorRun involves eight parallel processes with different ranks.
Introduction to GPU calculations in GPT-2 (124M)
- 🔄 Adapting Data Loading for Multi-Process Training- Adjusts data loading process to accommodate multiple processes.- Demonstrates how to assign different chunks of data to each process.- Ensures that each process works on a unique part of the dataset to maximize efficiency.
Initialization of GPT-2 model training process
- 🧩 Model Construction and Distributed Data Parallel (DDP)- Explanation of constructing a model for distributed training.- Wrapping the model into a DistributedDataParallel (DDP) container.- Understanding the behavior of DDP in forward and backward passes.
Wrapping the model into the Distributed Data Parallel container is important for constructing the M model.
- 🔄 Synchronization of Gradients in DDP- Discusses the synchronization of gradients in the DistributedDataParallel (DDP) setting.- Explanation of optimizing gradient synchronization to improve efficiency.- Implementation details for synchronizing gradients in DDP.
Avoiding context managers and code duplication by directly toggling the variable.
- 📉 Loss Averaging and Evaluation in DDP- Addressing the issue of loss averaging in the DDP setting.- Modifying code to compute and print the average loss across all processes.- Ensuring proper scaling of the number of tokens processed in the evaluation phase.
Printing loss over all processes and averaging it
GPT-2 (124M) reproduction process summary
datasets used in GPT-2, GPT-3, FineWeb (EDU)
* *** Dataset Selection:* The video discusses various datasets used for training large language models, ultimately choosing the FineWeb EDU dataset (10 billion token sample).
- 📚 Training Data Comparison: GPT-2 vs. GPT-3- Comparison of training datasets used in GPT-2 and GPT-3.- Description of web text and Common Crawl datasets utilized.- Introduction of alternative datasets like Red Pajamas, C4, Fine Web, and Fine Web Edu.
Training data mixtures are carefully curated and diverse.
- 📦 Preprocessing and Training Setup for Fine Web Edu- Overview of the preprocessing steps for the Fine Web Edu dataset.- Description of tokenization process and data shard creation.- Configuration adjustments in the data loader for using the Fine Web Edu dataset.
Tokenizing and processing large datasets for GPT-2 model training.
Sharding data for easier disk management
- 🧩 Script adjustments for GPT-3 replication- Adjusted data loader for processing multiple shards.- Set token processing rate and warm-up steps to match GPT-3 parameters.- Increased batch size optimization for faster training.
- 📊 Implementing validation evaluation- Added validation evaluation logic to the training loop.- Introduced periodic validation loss calculation.- Prepared for model comparison with GPT-2 124M.
Optimizing model training process for efficiency and quality.
validation data split, validation loss, sampling revive
* *** Validation Split:* A validation split is introduced to monitor overfitting and compare performance to the pre-trained GPT-2 model.
Evaluating GPT-2 (124M) model performance
- 🔄 Reorganizing sampling code- Moved sampling code closer to the main training loop.- Implemented a separate RNG for sampling to avoid impacting training RNG.- Addressed performance slowdown due to disabled Torch compile.
Troubleshooting torch compile issue
evaluation: HellaSwag, starting the run
* *** HellaSwag Evaluation:* The HellaSwag benchmark is implemented to evaluate the model's common sense reasoning abilities.
- 📈 Introducing H-SWAG evaluation- Described H-SWAG evaluation methodology and dataset.- Highlighted its role as a smooth evaluation metric.- Discussed implementation details for incorporating H-SWAG into the training script.
Language models trained with world knowledge outperform those with less training.
Construct batches of tokens with shared context and options for prediction
Model's inability to view all options at once
- 🔧 Adjustments to Training Script and Logging- Changes made to the training script to enable periodic evaluation and tracking of model performance over time.- Disabling torch compile due to issues with evaluation and sampling code.- Creation of a log directory to record training and validation losses, as well as H swag accuracies.
Running without torch compile affects code performance
- 📊 Evaluation of H Swag and Model Sampling- Introduction of code for evaluating H swag periodically during training.- Utilization of GPU collaboration for H swag evaluation.- Sampling from the model every 250th iteration for monitoring model progress.
Model training process overview
SECTION 4: results in the morning! GPT-2, GPT-3 repro
* *** Results:* After training for one epoch (10 billion tokens), the model surpasses the GPT-2 (124M) performance on HellaSwag, achieving comparable results with 10x fewer training tokens.
- 📈 Training Progress Visualization- Visualization of training progress using Matplotlib.- Analysis of loss curves and model performance.- Comparison of model performance against GPT-2 and GPT-3 accuracy metrics.
GPT-2 (124M) trained on 10 billion tokens matching or surpassing accuracy of GPT-2 (100B) trained on significantly fewer tokens
- 🧠 Reflections on Training Results and Data Quality- Discussion on the implications of achieving GPT-3 level accuracy with fewer tokens.- Consideration of factors influencing model performance, such as data distribution and dataset quality.- Reflections on potential improvements in data preprocessing and model hyperparameters.
Issue with data shuffling affecting model training
* *** Overnight Run:* Training for four epochs (40 billion tokens) further improves HellaSwag accuracy, approaching the GPT-3 (124M) performance.
- ⚙ Optimization Techniques and Training Efficiency- Examination of optimization issues and periodicity in data loading.- Discussion on the impact of learning rate adjustments on training efficiency.- Consideration of techniques to improve data shuffling and reduce data dependency.
Improving data shuffling and model efficiency.
- 🛠 Model Fine-Tuning and Future Directions- Overview of fine-tuning process for conversational AI applications.- Introduction of model checkpointing for resuming optimization and model evaluation.- Discussion on alternative evaluation methods and comparison with GPT-2 and GPT-3.
Training model to mimic GPT-3 with sequence length adjustment
Comparison between nanog GPT in PyTorch and llm Doc and lm. C CUDA implementation
shoutout to llm.c, equivalent but faster code in raw C/CUDA
* *** Shoutout to llm.c:* The video showcases "llm.c," a faster C/CUDA implementation of GPT-2/3 training.
Comparing PyTorch and lm.C performance for training GPT-2 and GPT-3.
summary, phew, build-nanogpt github repo
