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On Zen4 repack fp16 models to bf16_r16 when run-time-repacking is requested
This massively improves performance. As this is opt-in, we do not worry about possible precision loss in the f16 -> bf16 conversion.
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@@ -5260,6 +5260,7 @@ inline ggml_bf16_t to_bf16(const float& x) {
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helper.f = x;
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return ggml_bf16_t{(uint16_t)(helper.u >> 16)};
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}
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inline ggml_bf16_t to_bf16(const ggml_half& x) { return to_bf16(GGML_FP16_TO_FP32(x)); }
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inline ggml_bf16_t to_bf16(const ggml_bf16_t& x) { return x; }
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template <typename T>
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void repack_bf16(int nrows, int n_per_row, const T * x, ggml_bf16_t * y) {
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@@ -6082,7 +6083,8 @@ void iqk_repack_tensor(struct ggml_tensor * tensor) {
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{ GGML_TYPE_Q8_0, { GGML_TYPE_Q8_0_R4, 4, (Repack::repack_func)repack_q8_0} },
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{ GGML_TYPE_Q8_K, { GGML_TYPE_Q8_K_R8, 8, (Repack::repack_func)repack_q8_k} },
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#ifdef __AVX512BF16__
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{ GGML_TYPE_BF16, { GGML_TYPE_BF16_R16, 16, (Repack::repack_func)repack_bf16<ggml_bf16_t>} },
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{ GGML_TYPE_BF16, { GGML_TYPE_BF16_R16, 16, (Repack::repack_func)repack_bf16<ggml_bf16_t>}},
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{ GGML_TYPE_F16, { GGML_TYPE_BF16_R16, 16, (Repack::repack_func)repack_bf16<ggml_half>} },
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#endif
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};
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