AI Coffee Break with Letitia - 4-Bit Training for Billion-Parameter LLMs? Yes, Really.
The discussion focuses on training large language models (LLMs) using FP4 quantization, which reduces the precision of model weights and activations to just four bits. This approach significantly cuts down on computational costs, including energy, time, and memory, while maintaining performance comparable to higher precision formats like BF16. The main challenge with FP4 is the quantization error, which the researchers addressed by using hybrid precision for sensitive parts of the training process and employing gradient estimators to allow backpropagation through quantization. They demonstrated the effectiveness of this method by training a 13 billion parameter Llama model on 100 billion tokens, achieving performance that matches or even slightly outperforms BF16 on various benchmarks. Although current hardware does not support native FP4, future GPUs like Nvidia's Blackwell are expected to enable significant speedups and efficiency gains, making this approach viable for smaller labs and startups.
Key Points:
- FP4 quantization reduces model weights and activations to four bits, cutting computational costs.
- Hybrid precision is used for sensitive training parts to maintain stability.
- Gradient estimators enable backpropagation through quantization, overcoming FP4's limitations.
- The method was tested on a 13 billion parameter model, showing performance comparable to BF16.
- Future hardware support for FP4 could make this approach widely accessible and efficient.
Details:
1. 🔍 Exploring Low Precision Training for LLMs
- Most LLMs are trained using 32bit or 16bit floating point precision, which is computationally expensive in terms of energy, time, memory, and cost.
- Quantization of model weights into low precision formats like 8 or 4bit is already used for LLM inference, with two-bit and binary quantization available for cheap post-training runs.
- The new paper explores using FP4 quantization to squeeze model weights and activations into just four bits during training with minimal performance loss.
- Over 90% of the training cost for LLMs is due to matrix multiplications, which can be accelerated using 4bit numbers, leading to better GPU core utilization, cache usage, and lower memory bandwidth.
- Training with FP4 can potentially provide significant speedups if supported by hardware, but it introduces massive quantization error, making naive FP4 training ineffective.
- The researchers managed to make FP4 work effectively, sometimes outperforming 16bit precision on benchmarks, which will be explained later.
2. 📚 Skill Up with Simply Learn
- Skill Up is a free learning platform launched by Simply Learn, offering self-paced courses in AI, generative AI, data science, and cloud computing.
- Courses are crafted by industry giants like Google, Microsoft, and AWS.
- The platform includes practical courses on essential tools like the Hugging Face Python library and retrieval augmented generation techniques.
- Learners receive free certificates upon course completion, enhancing job readiness.
- Skill Up provides additional resources on career paths, salaries, interview preparation, and job-ready skills.
- Courses such as 'Hugging Face Python Library' and 'Retrieval Augmented Generation' help in mastering cutting-edge tools.
- User testimonials highlight improved job readiness and career advancement after course completion.
- Specific success stories include users who transitioned into roles at major tech firms after using Skill Up.
3. ⚡ Benefits of Low Precision in Training
- Training large language models with billions or trillions of parameters is expensive in terms of compute, energy, and money, but using fewer bits to represent numbers can significantly reduce costs.
- Multiplying 4-bit numbers with the right CUDA kernels and hardware can be done in the time and memory it would take to process a 32-bit pair, offering huge gains in throughput and efficiency.
- Transitioning from FP32 (32-bit) to BF16 (16-bit) halves memory usage and increases training speed, especially with GPUs like Nvidia's A100 or H100s.
- FP8 (8-bit) formats offer even faster performance despite their limited range, allowing training at a fraction of the cost with similar accuracy to FB16 if the model and pipeline are carefully designed.
- Quantization during training involves converting FP16 model weights and activations to an 8-bit range, using FP8 for forward passes and updating weights in FP16 during backward passes.
- The UUP paper suggests designing models so activations naturally stay within the FP8 range, avoiding complicated dynamic rescaling and benefiting post-training quantization.
- FP4 training is challenging due to representing only 16 distinct values, making subtle gradient adjustments difficult, but recent methods combine quantization strategies with hybrid precision and gradient estimators to train large models effectively.
4. 🔧 Techniques for FP4 Training
- Matrix multiplication is identified as the biggest computational bottleneck in training, accounting for more than 95% of total compute.
- The authors propose performing matrix multiplications in FP4, requiring weights and activations to be quantized to FP4, while sensitive parts like weight updates and optimizer states remain in FP8 or FP16 for precision.
- This approach constitutes a mixed precision training setup, where the model weights are quantized to FP4 at each training step using the appsmax function.
- The appsmax function scales values in the tensor relative to the maximum absolute value, ensuring that quantization happens at every training step due to weight updates being computed on an FP16 master copy.
- An FP16 master copy of weights is maintained to store small floating point changes during weight updates, preventing rounding errors and loss of information that FP4 would incur.
- The process involves computing in FP4 for speed and updating in FP16 for stability, achieving efficient ultra-low precision training.
5. 🔍 Addressing Activation Quantization Challenges
- Activation quantization poses more challenges than weight quantization due to the unpredictable nature of activation outputs, which result from the multiplication of weights and inputs.
- Dynamic range issues arise because outlier values in activations can significantly stretch the range, making most other activation values appear small when quantized.
- In FP4 quantization, these outliers can lead to rounding errors where activations are incorrectly rounded to zero, causing information loss.
- To address this, the technique of outlier clamping is employed, where the top 0.1% of activation values are clamped to reduce the dynamic range.
- The residuals from clamping are preserved in a sparse matrix and processed separately in high precision, ensuring that essential information is not lost during quantization.
6. 🔄 Overcoming Backward Pass Limitations
- Backward pass quantization functions like appsmax are not differentiable, posing challenges for effective backpropagation in neural networks.
- The straight-through estimator (ST) has been a common workaround by ignoring quantization during gradient computation, but it results in issues, especially at low bit widths like FP4, due to its crude approximation.
- The authors propose a new differentiable gradient estimator that uses hard quantization in the forward pass and a smooth differentiable function in the backward pass to approximate quantization.
- This approach enhances convergence and stability by providing a more accurate gradient signal, akin to sliding down a ramp rather than falling off a cliff, thus improving the training process for neural networks.
7. 📈 FP4 Training Results and Benchmarks
- Matrix multiplications are executed in FP4, optimizing computational efficiency and reducing resource consumption compared to higher precision formats.
- Weight updates, gradients, and optimizer states leverage FP8 or FP16, providing a balance between precision and computational speed, leading to improved training times without sacrificing accuracy.
- Quantization uses a non-differentiable approximation in the forward pass to enhance computational speed, while maintaining accuracy in the backward pass through simulated differentiability.
- Activations are stabilized using techniques such as outlier clamping and sparse compensation, ensuring training stability and preventing divergence.
- The use of FP4 in matrix multiplications has shown to reduce power consumption by up to 30%, demonstrating significant energy efficiency.
- Training models with FP4 has resulted in up to 25% faster convergence times compared to traditional precision formats.
8. 🚀 Future of FP4 with Upcoming Hardware
- FP4 training framework was tested on model sizes of 1.3 billion, 7 billion, and 13 billion parameters using 100 billion tokens, showing training curves nearly identical to BF-16.
- FP4's performance was evaluated zero-shot across various benchmarks, consistently matching or slightly outperforming BF-16, with an average accuracy of 54.95% for FP4 versus 54.44% for BF-16 in the 13 billion size model.
- Experiments used FP8 hardware to emulate FP4 since no current GPU supports native FP4 tensor cores, leading to slower processing due to custom casting and lookup operations.
- Nvidia's upcoming Blackwell GPUs will support native FP4 compute, potentially doubling throughput compared to FB8 and reducing memory and energy usage, making large-scale training more accessible.
- Training in 4-bit precision (FP4) is shown to be possible and practically viable, challenging assumptions about necessary resources for training powerful models.