ikawrakow/ik_llama.cpp
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Offload Bitnet token embeddings to the GPU (#1)

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* bitnet: put token embeddings on the GPU

* Update README with the new CUDA/Meat performance

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This commit is contained in:
Kawrakow
2024-07-26 09:41:04 +02:00
committed by GitHub
parent 4673de8cbe
commit a14a9426ec
2 changed files with 46 additions and 32 deletions
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18
llama.cpp
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@@ -5355,8 +5355,22 @@ static bool llm_load_tensors(
bool use_mmap_buffer = true;
// there is very little benefit to offloading the input layer, so always keep it on the CPU
model.buft_input = llama_default_buffer_type_cpu(true);
//model.buft_input = llama_default_buffer_type_offload(main_gpu);
//model.buft_input = llama_default_buffer_type_cpu(true);
//
// Well, this is not really true when the model uses the same tensor for token embeddings and for output
// (e.g., Bitnet, Gemma). If we use the above, then the matrix multiplication with the output tensor runs
// on the CPU, which can have quite a significant impact on performance. For instance, for 3B-Bitnet, I get
// TG-128 = ~240 t/s on an RTX-4080 with the above, and TG-128 = 320 t/s with the version below.
// The issue with just generically putting token embeddings on the GPU is that CUDA supports the GET_ROWS
// operation only for F16 and legacy quants, and this leads to a massive drop in performance when token embeddings
// are quantized with a k- or i-quant (which is almost always true). The back-end related stuff and offloading
// to the GPU has become quite opaque and hard to understand, so for now we fix this just for Bitnet
// (where token_embeddings is quantized with Q8_0).
if (model.arch == LLM_ARCH_BITNET) {
model.buft_input = llama_default_buffer_type_offload(model, main_gpu);
} else {
model.buft_input = llama_default_buffer_type_cpu(true);
}
model.buft_layer.resize(n_layer);