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dev note on tensor encoding LUT

brian khuu 2024-06-13 11:28:39 +10:00
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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
```