vbatts/llama.cpp
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added the llama_v2 cuda back (+2 squashed commit)

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Squashed commit:

[1c97fd4] Revert "fix for cublas"

This reverts commit 994be9a4db.

[fce03c3] Revert "fix for cublas"

This reverts commit 33528f5b1d.
This commit is contained in:
Concedo 2023-06-11 23:18:03 +08:00
parent fb67506c1b
commit c44b9c3ecf
1 changed files with 31 additions and 3 deletions
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34
otherarch/llama_v2.cpp
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@ -9,7 +9,9 @@
#include "llama_v2.h"
#include "ggml_v2.h"
#if defined(GGML_USE_CLBLAST)
#ifdef GGML_USE_CUBLAS
#include "ggml_v2-cuda.h"
#elif defined(GGML_USE_CLBLAST)
#include "ggml_v2-opencl.h"
#endif
@ -892,7 +894,7 @@ static const char *llama_v2_model_type_name(e_model2 type) {
case MODEL_13B_2: return "13B";
case MODEL_30B_2: return "30B";
case MODEL_65B_2: return "65B";
default:
default:
printf("\nWARNING: NON-STANDARD LLAMA FILE DETECTED. DEFAULT TO 7B SIZE.\n");
return "UNKNOWN";
}
@ -1058,7 +1060,33 @@ static void llama_v2_model_load_internal(
ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL);
model.mapping = std::move(ml->mapping);
#if defined(GGML_USE_CLBLAST)
#if defined(GGML_USE_CUBLAS)
{
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
fprintf(stderr, "%s: [old cublas] offloading %d layers to GPU\n", __func__, n_gpu);
size_t vram_total = 0;
for (int i = 0; i < n_gpu; ++i) {
const auto & layer = model.layers[i];
ggml_v2_cuda_transform_tensor(layer.wq); vram_total += ggml_v2_nbytes(layer.wq);
ggml_v2_cuda_transform_tensor(layer.wk); vram_total += ggml_v2_nbytes(layer.wk);
ggml_v2_cuda_transform_tensor(layer.wv); vram_total += ggml_v2_nbytes(layer.wv);
ggml_v2_cuda_transform_tensor(layer.wo); vram_total += ggml_v2_nbytes(layer.wo);
ggml_v2_cuda_transform_tensor(layer.w1); vram_total += ggml_v2_nbytes(layer.w1);
ggml_v2_cuda_transform_tensor(layer.w2); vram_total += ggml_v2_nbytes(layer.w2);
ggml_v2_cuda_transform_tensor(layer.w3); vram_total += ggml_v2_nbytes(layer.w3);
}
if (n_gpu_layers > (int) hparams.n_layer) {
fprintf(stderr, "%s: [old cublas] offloading output layer to GPU\n", __func__);
ggml_v2_cuda_transform_tensor(model.output); vram_total += ggml_v2_nbytes(model.output);
}
fprintf(stderr, "%s: [old cublas] total VRAM used: %zu MB\n", __func__, vram_total / 1024 / 1024);
}
#elif defined(GGML_USE_CLBLAST)
{
const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer));
if(GetQuantsUnshuffled())