GGUF fp32/fp16 conversion to checkpoint #134
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Summary: Only works for fp32 and fp16 types so that means, It isn't providing much value right now because `convert_hf_checkpoint.py` can directly generate an equivalent .pth checkpoint file without gguf format indirection. In the future, we will support running the quantized version of the graph. Test Plan: 0. Setup pip install gguf [email protected]:ggerganov/llama.cpp.git 1. Preparation: convert existing hf model to fp16 `python llama.cpp/convert.py [HF-dir] --outtype f16`` which will generate [HF-dir]/ggml-model-f16.gguf 2. Convert GGUF file to a checkpoint `python scripts/convert_from_gguf.py --gguf_file [HF-dir]/ggml-model-f16.gguf --checkpoint_file [HF-dir]/model_gguf.pth` 3. Validate that it works: `python generate.py --checkpoint_path [HF-dir]/model_gguf.pth --device=cpu --prompt "Hello, my name is" --max_new_tokens 20`
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Why import GGUF when one can do decode in place using native PyTorch, see https://github.com/malfet/llm_experiments/blob/74a935344fbce5680dbd2dafc7dfd95231303444/run_llama.py#L447 |
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Summary:
Only works for fp32 and fp16 types so that means it isn't providing much value right now.
convert_hf_checkpoint.pycan already directly generate an equivalent .pth checkpoint file without gguf format indirection.However this PR just creates the foundation and validation that the basic fp32 and fp16 works fine. In the future, we will support running the quantized version of the gguf graph in eager.
Test Plan:
pip install ggufgit clone [email protected]:ggerganov/llama.cpp.gitpython scripts/download.py --repo_id [HF-dir]python llama.cpp/convert.py [HF-dir] --outtype f16`` which will generate [HF-dir]/ggml-model-f16.ggufpython scripts/convert_from_gguf.py --gguf_file [HF-dir]/ggml-model-f16.gguf --checkpoint_file [HF-dir]/model_gguf.pthpython generate.py --checkpoint_path [HF-dir]/model_gguf.pth --device=cpu --prompt "Hello, my name is" --max_new_tokens 20