Bitnet changes (#106)

* Adapting iq2_bn to work without separate scale tensors

Why? It is becoming burdensome to maintain the special Bitnet
conversion in convert_hf_to_gguf.py, so I thnk it is better
to make iq1_bn and iq2_bn just work with the mainline
conversion script (which does not generate scales).

* Adapting iq1_bn to work without separate scale tensors

* Adapting iq2_bn: CUDA dequantize

* Adapting iq2_bn: CUDA works

* Adapting iq1_bn: CUDA works

* Adapting iq1_bn, iq2_bn: NEON

* Adapting iq1_bn, iq2_bn: Metal

Dequantize works, but there is still something wrong
with the dot products.

* WIP

Absoolutely don't see what is wrong with the iq1_bn and iq2_bn
vector dot product kernels.

* Remove iq1_tn and iq2_tn - Part 1

Now that iq1_bn and iq2_bn have per row scales, there is no
reason to also have iq1_tn and iq2_tn.

* Remove iq1_tn and iq2_tn - Part 2

* Bitnet: use the standard llm_build_kv to build self attention

My main motivation was to enable FA. But FA does not work anyway
because head size is 100 for the Botnet ternary models
(and I had forgotten this little detail).

* Revert "Avoid rebuild of GGML graph for each token (#98)"

This reverts commit f2d315b46f.
As far as I can tell, the commit breaks Metal TG.

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>

ggml/include/ggml-backend.h

@@ -232,12 +232,6 @@ extern "C" {
     GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
     GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor);
 
-    // Utility to query whether cached GGML graph is in use
-    GGML_API bool ggml_use_cached_graph(ggml_backend_sched_t sched);
-
-    // Set whether or not to use GGML graph caching
-    GGML_API void ggml_set_cached_graph(ggml_backend_sched_t sched, bool set_value);
-
 
 #ifdef  __cplusplus
 }