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2dd680b799
* Improve the handling of quantized weights Handling of quantized weights was split between two mechanisms: - For quantized checkpoints, we used the new weight loader infrastructure. - For quantization while loading (EETQ, FP8, bitsandbytes) we instead relied on conditional in `get_linear`. Weight loaders support context managers to selectively load particular layers with different weight loaders, which is useful for models like Idefics2 AWQ, which uses a quantized text model, but unquantized vision and connector models. However, the context manager would be overrided by `get_linear`, which string-checks `quantizer`. Also, the context manager would not work with EETQ, FP8, and bitsandbytes. This change migrates all quantizers to the weight loader infrastructure. This has several benefits: - We can use context managers with all quantizers. - All the implementation details move down to the quantizer layers, `get_linear` does not need to know how to handle quantizer linear layers. - All quantizer weights are strongly typed, we don't pass around raw tensors. - We don't have to pass around the `quantizer` string everywhere. * Exclude non-MLP layers when using FP8 quantization with Llama |
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| .. | ||
| custom_kernels | ||
| exllama_kernels | ||
| exllamav2_kernels | ||
| marlin | ||
| tests | ||
| text_generation_server | ||
| .gitignore | ||
| Makefile | ||
| Makefile-awq | ||
| Makefile-eetq | ||
| Makefile-flash-att | ||
| Makefile-flash-att-v2 | ||
| Makefile-lorax-punica | ||
| Makefile-selective-scan | ||
| Makefile-vllm | ||
| poetry.lock | ||
| pyproject.toml | ||
| README.md | ||
| requirements_cuda.txt | ||
| requirements_intel.txt | ||
| requirements_rocm.txt | ||
Text Generation Inference Python gRPC Server
A Python gRPC server for Text Generation Inference
Install
make install
Run
make run-dev