dev note on tensor encoding LUT

dev-notes.md

@@ -60,3 +60,116 @@ Aka it's for the writing/reading api.
 There is this cpp example program that will write a test gguf write/read
 
 - [./example/gguf.cpp](https://github.com/ggerganov/llama.cpp/blob/master/examples/gguf/gguf.cpp)
+
+
+### If we don't store the size tensor array elements etc in gguf where do we store these?
+
+In ggml.c refer to `static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT]`
+which is a lookup table containing enough information to deduce the size of a tensor layer
+in bytes if given an offset and element dimension count.
+
+One good example is shown below (but annotated for clarity):
+
+```c
+static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
+...
+    [GGML_TYPE_F16] = {
+        // General Specs About This Tensor Encoding Scheme
+        .type_name                = "f16",
+        .blck_size                = 1,
+        .type_size                = sizeof(ggml_fp16_t),
+        .is_quantized             = false,
+
+        // C function methods for interpreting the blocks 
+        .to_float                 = (ggml_to_float_t) ggml_fp16_to_fp32_row,
+        .from_float               = (ggml_from_float_t) ggml_fp32_to_fp16_row,
+        .from_float_reference     = (ggml_from_float_t) ggml_fp32_to_fp16_row,
+
+        // C functions methods plus extra specs required for dot product handling
+        .vec_dot                  = (ggml_vec_dot_t) ggml_vec_dot_f16,
+        .vec_dot_type             = GGML_TYPE_F16,
+        .nrows                    = 1,
+    },
+...
+}
+```
+
+So basically these are used in various places to help allow the developers to
+get a sense of the tensor encoding spec and sizing as you can see with the
+getter methods below (Note didn't trace fully the other functions directly using
+the values within ggml.c, the few in this graph is just for illustrative purpose):
+
+```mermaid
+graph LR;
+    type_traits{"type_traits[]\n Lookup Table"}
+    type_traits-->type_name
+    type_traits-->blck_size
+    type_traits-->type_size
+    type_traits-->is_quantized
+    %%type_traits-->to_float
+    %%type_traits-->from_float
+    %%type_traits-->from_float_reference
+    %%type_traits-->vec_dot
+    %%type_traits-->vec_dot_type
+    %%type_traits-->nrows
+
+    subgraph getter functions / methods
+        ggml_type_name(["ggml_type_name()"])
+        ggml_blck_size(["ggml_blck_size()"])
+        ggml_type_size(["ggml_type_size()"])
+        ggml_is_quantized(["ggml_is_quantized()"])
+    end
+    type_name --> ggml_type_name(["ggml_type_name()"])
+    blck_size --> ggml_blck_size(["ggml_blck_size()"])
+    type_size --> ggml_type_size(["ggml_type_size()"])
+    is_quantized --> ggml_is_quantized(["ggml_is_quantized()"])
+
+    blck_size --> ggml_type_sizef(["ggml_type_sizef()"])
+    blck_size --> ggml_quantize_chunk(["ggml_quantize_chunk()"])
+```
+
+This is how the LUT is used to convert a tensor data area to/from float for processing
+(However these methods is not used in the GPU if i understand as these data area is processed directly using GPU specific instruction code.
+This is also why the tensors elements has to be packed in a certain way.)
+
+The below analysis is only for connections within ggml.c
+
+```mermaid
+graph LR;
+    type_traits{"type_traits[]\n Lookup Table"}
+    %%type_traits-->type_name
+    %%type_traits-->blck_size
+    %%type_traits-->type_size
+    %%type_traits-->is_quantized
+    type_traits-->to_float
+    type_traits-->from_float
+    type_traits-->from_float_reference
+    %%type_traits-->vec_dot
+    %%type_traits-->vec_dot_type
+    %%type_traits-->nrows
+
+    ggml_compute_forward_add_q_f32(["ggml_compute_forward_add_q_f32()"])
+    to_float --> ggml_compute_forward_add_q_f32
+    ggml_compute_forward_out_prod_q_f32(["ggml_compute_forward_out_prod_q_f32()"])
+    to_float --> ggml_compute_forward_out_prod_q_f32
+    ggml_compute_forward_get_rows_q(["ggml_compute_forward_get_rows_q()"])
+    to_float --> ggml_compute_forward_get_rows_q
+    ggml_compute_forward_flash_attn_ext_f16(["ggml_compute_forward_flash_attn_ext_f16()"])
+    to_float --> ggml_compute_forward_flash_attn_ext_f16
+
+    ggml_compute_forward_dup_f16(["ggml_compute_forward_dup_f16()"])
+    from_float --> ggml_compute_forward_dup_f16
+    ggml_compute_forward_dup_bf16(["ggml_compute_forward_dup_bf16()"])
+    from_float --> ggml_compute_forward_dup_bf16
+    ggml_compute_forward_dup_f32(["ggml_compute_forward_dup_f32()"])
+    from_float --> ggml_compute_forward_dup_f32
+    ggml_compute_forward_add_q_f32(["ggml_compute_forward_add_q_f32()"])
+    from_float --> ggml_compute_forward_add_q_f32
+    ggml_compute_forward_mul_mat(["ggml_compute_forward_mul_mat()"])
+    from_float --> ggml_compute_forward_mul_mat
+    ggml_compute_forward_mul_mat_id(["ggml_compute_forward_mul_mat_id()"])
+    from_float --> ggml_compute_forward_mul_mat_id
+    ggml_compute_forward_flash_attn_ext_f16(["ggml_compute_forward_flash_attn_ext_f16()"])
+    from_float --> ggml_compute_forward_flash_attn_ext_f16
+```
+