The recent release of go-attention, a pure Go implementation of attention mechanisms and transformer layers, has sparked significant debate within the developer community about the performance limitations of Go for computationally intensive tasks like machine learning.
While the library aims to provide a clean implementation without external dependencies, community feedback suggests it may face substantial performance challenges compared to alternatives in languages with better support for hardware acceleration.
Performance Concerns Dominate Discussion
Developers in the community have raised serious concerns about the performance implications of implementing attention mechanisms in pure Go. Multiple commenters pointed out that Go lacks native SIMD (Single Instruction, Multiple Data) support, which is crucial for the vector and matrix operations that form the backbone of transformer models. One developer noted that Go is unacceptably slow when it comes to math and that without SIMD CPU instructions, the implementation could be orders of magnitude slower than equivalent code in C, C++, Rust, or even Python and Java (which typically call optimized C/C++ libraries).
The absence of GPU/CUDA support in the library was also highlighted as a significant limitation, as modern transformer implementations rely heavily on GPU acceleration for practical performance. Some developers suggested that using Go bindings for established libraries like llama.cpp would be a more pragmatic approach for production use cases.
Performance Limitations Highlighted by Community:
- Lack of native SIMD (Single Instruction, Multiple Data) support
- No GPU/CUDA acceleration
- Scalar code operating on single fp64 values
- Potentially "orders of magnitude slower" than C/C++/Rust implementations
Suggested Alternatives:
- Using Go bindings for llama.cpp
- Implementing critical sections in Go assembly (Goasm)
- Using specialized packages like viterin/vek or kelindar/simd
- Using Go as a JIT code generator with cgo
Alternative Approaches and Workarounds
Several developers offered potential solutions to the performance bottleneck. One suggestion involved using Go's assembly support (Goasm) to implement critical mathematical operations. Others mentioned existing Go packages like viterin/vek or kelindar/simd that could help improve performance through better SIMD utilization.
An interesting alternative approach was proposed by one developer who described using Go as a JIT code generator, dynamically linking the result, and jumping into it with cgo to achieve better performance that easily saturates the CPU vector math units. This highlights the creative workarounds developers are exploring to overcome Go's native performance limitations for mathematical operations.
Educational Value Despite Performance Issues
Despite performance concerns, some community members appreciated the educational value of the implementation. One commenter noted that the library provides an opportunity to study [attention mechanisms] at the implementation level as opposed to reading blogs. This suggests that even if the library isn't suitable for production workloads, it serves an important purpose in making these algorithms more accessible and understandable to Go developers.
When analyzing the code with tools like Ghidra, developers confirmed that the implementation compiles to scalar code operating on single fp64 values, reinforcing the performance concerns. The consensus appears to be that while the library might be useful for learning and prototyping, production applications would likely require alternative approaches that can leverage hardware acceleration.
The discussion around go-attention reflects a broader tension in the Go ecosystem between the language's emphasis on simplicity and readability versus the performance demands of computationally intensive domains like machine learning. As one developer put it:
Without using SIMD CPU instructions, its gonna be super expensive.
This debate also touches on the growing interest in implementing machine learning primitives in languages beyond Python, as developers seek to integrate these capabilities into their existing technology stacks without the overhead of cross-language interoperability.
Reference: go-attention: A full attention mechanism and transformer in pure go