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authorKawrakow <48489457+ikawrakow@users.noreply.github.com>2024-07-27 07:55:01 +0200
committerGitHub <noreply@github.com>2024-07-27 07:55:01 +0200
commit154e0d75fccf1784fe9ff6fd76a630b66563da3d (patch)
tree81ce6dbb5b1900c1aa78a879f0593c694cab9d27 /examples/cvector-generator/pca.hpp
parent0684c3e9c70d49323b4fc517128cbe222cab7f96 (diff)
Merge mainline llama.cpp (#3)
* Merging mainline - WIP * Merging mainline - WIP AVX2 and CUDA appear to work. CUDA performance seems slightly (~1-2%) lower as it is so often the case with llama.cpp/ggml after some "improvements" have been made. * Merging mainline - fix Metal * Remove check --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Diffstat (limited to 'examples/cvector-generator/pca.hpp')
-rw-r--r--examples/cvector-generator/pca.hpp5
1 files changed, 4 insertions, 1 deletions
diff --git a/examples/cvector-generator/pca.hpp b/examples/cvector-generator/pca.hpp
index 36eadaac..6ec3141a 100644
--- a/examples/cvector-generator/pca.hpp
+++ b/examples/cvector-generator/pca.hpp
@@ -290,7 +290,7 @@ static void power_iteration(
}
printf("%s: layer %d/%d, iteration: %d / total: %d (batch = %d) ...\n",
- __func__, params.i_layer+1, params.n_layers, iter, n_iters, params.n_batch);
+ __func__, params.i_layer+1, params.n_layers, iter+1, n_iters, params.n_batch);
}
// get output tensor
@@ -298,6 +298,9 @@ static void power_iteration(
ggml_backend_tensor_get(last_eigenvector, output->data, 0, ggml_nbytes(last_eigenvector));
//print_debug_tensor(output);
ggml_gallocr_free(allocr);
+
+ // TODO @ngxson : The output vector is randomly inverted
+ // Solution: https://github.com/ggerganov/llama.cpp/pull/8069#issuecomment-2185328171
}
static void run_pca(