Update Makefile var + add comment

diff --git a/Makefile b/Makefile
index 862d02be0540c85daa68a3d3f196932d084e5a22..8388c290d75ceff7ef8ba7424bc27608e6574c79 100644 (file)
--- a/Makefile
+++ b/Makefile
@@ -125,18 +125,18 @@ ifneq ($(filter ppc64%,$(UNAME_M)),)
                CXXFLAGS += -std=c++23 -DGGML_BIG_ENDIAN
        endif
 endif
-ifndef WHISPER_NO_ACCELERATE
+ifndef LLAMA_NO_ACCELERATE
        # Mac M1 - include Accelerate framework
        ifeq ($(UNAME_S),Darwin)
                CFLAGS  += -DGGML_USE_ACCELERATE
                LDFLAGS += -framework Accelerate
        endif
 endif
-ifdef WHISPER_OPENBLAS
+ifdef LLAMA_OPENBLAS
        CFLAGS  += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas
        LDFLAGS += -lopenblas
 endif
-ifdef WHISPER_GPROF
+ifdef LLAMA_GPROF
        CFLAGS   += -pg
        CXXFLAGS += -pg
 endif

diff --git a/README.md b/README.md
index 2486d4dca3ee235e17e8fdb388540f15d82aef88..26f8cb5915046edaaccea85697e808b8f838476f 100644 (file)
--- a/README.md
+++ b/README.md
@@ -180,5 +180,11 @@ When running the larger models, make sure you have enough disk space to store al
   In general, it seems to work, but I think it fails for unicode character support. Hopefully, someone can help with that
 - I don't know yet how much the quantization affects the quality of the generated text
 - Probably the token sampling can be improved
-- x86 quantization support [not yet ready](https://github.com/ggerganov/ggml/pull/27). Basically, you want to run this on Apple Silicon. For now, on Linux and Windows you can use the F16 `ggml-model-f16.bin` model, but it will be much slower.
+- x86 quantization support [not yet ready](https://github.com/ggerganov/ggml/pull/27). Basically, you want to run this
+  on Apple Silicon. For now, on Linux and Windows you can use the F16 `ggml-model-f16.bin` model, but it will be much
+  slower.
+- The Accelerate framework is actually currently unused since I found that for tensors shapes typical for the Decoder,
+  there is no benefit compared to the ARM_NEON intrinsics implementation. Of course, it's possible that I simlpy don't
+  know how to utilize it properly. But in any case, you can even disable it with `LLAMA_NO_ACCELERATE=1 make` and the
+  performance will be the same, since no BLAS calls are invoked by the current implementation