llama.cpp

Commit Graph

Author	SHA1	Message	Date
Timmy Knight	20a1a4e09c	Fix GPTQ converter (#423 ) * Fix GPTQ converter * Fix comment --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	1 year ago
comex	16ffc013c6	Importer for GPTQ quantized LLaMA models (#301 ) * [WIP, broken] Importer for GPTQ quantized LLaMA models Based on: https://github.com/qwopqwop200/GPTQ-for-LLaMa Current status: Something is busted. The output starts out decent, but quickly degrades into gibberish. This doesn't happen with either the original GPTQ-for-LLaMa using the same weights, or llama.cpp when using weights quantized by its own quantizer. Is there a bug in the conversion script that somehow only comes into play with a large context size? I did notice one potential issue. It's clearly not the main cause of the gibberish, since it doesn't happen when using q4_1 weights quantized by llama.cpp itself, but it seems concerning. When doing a matrix multiplication of f16 * f32 => f32 or q4_1 * f32 => f32, at least when the multiplication is not done with BLAS, the intermediate results are stored in the smaller format rather than f32. This seems like an unnecessary waste of precision, especially in the q4_1 case. I was originally hoping to validate the results by matching the Python implementation's output exactly, but precision and non-associativity issues make this very difficult, including when performing matrix multiplications and, especially, computing norms. Anyway, design details: The models being imported store per-layer weights in essentially q4_1 format, although the addend and scale are shared across an entire row rather than every group of 32 weights. This script duplicates the addend and scale to match ggml's expectations, at the cost of wasting some memory. However, there are two differences which I accommodated changing the output format (and adding corresponding support to main.cpp) rather than having the script match the existing one: - The tok_embeddings and output weights (i.e. the weights that aren't per-layer) are f16 instead of q4_1. They could be converted to q4_1, and the impact of the loss of precision would probably be low, but this would rule out exactly matching the Python implementation's output for validation. - There is no sharding, since the input doesn't have it, and for a CPU-only implementation it seems more useful to avoid having to deal with multiple files. The new format is differentiated from existing q4_1 format by changing the 'f16' header flag to a new value, 4. That said, I think a cleaner approach would be to change main.cpp to support loading each tensor with an arbitrary sharding configuration and type rather than hardcoding specific combinations of types. So far I've wasted too much time debugging to try implementing this... * Add missing permutation. Now it works. --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>	1 year ago

Author

SHA1

Message

Date

Timmy Knight

20a1a4e09c

Fix GPTQ converter (#423 )

* Fix GPTQ converter

* Fix comment

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

comex

16ffc013c6

Importer for GPTQ quantized LLaMA models (#301 )

* [WIP, broken] Importer for GPTQ quantized LLaMA models

Based on: https://github.com/qwopqwop200/GPTQ-for-LLaMa

Current status: Something is busted.  The output starts out decent, but
quickly degrades into gibberish.  This doesn't happen with either the
original GPTQ-for-LLaMa using the same weights, or llama.cpp when using
weights quantized by its own quantizer.  Is there a bug in the
conversion script that somehow only comes into play with a large context
size?

I did notice one potential issue.  It's clearly not the main cause of
the gibberish, since it doesn't happen when using q4_1 weights quantized
by llama.cpp itself, but it seems concerning.  When doing a matrix
multiplication of f16 * f32 => f32 or q4_1 * f32 => f32, at least when
the multiplication is not done with BLAS, the intermediate results are
stored in the smaller format rather than f32.  This seems like an
unnecessary waste of precision, especially in the q4_1 case.

I was originally hoping to validate the results by matching the Python
implementation's output exactly, but precision and non-associativity
issues make this very difficult, including when performing matrix
multiplications and, especially, computing norms.

Anyway, design details:

The models being imported store per-layer weights in essentially q4_1
format, although the addend and scale are shared across an entire row
rather than every group of 32 weights.  This script duplicates the
addend and scale to match ggml's expectations, at the cost of wasting
some memory.

However, there are two differences which I accommodated changing the
output format (and adding corresponding support to main.cpp) rather than
having the script match the existing one:

- The tok_embeddings and output weights (i.e. the weights that aren't
  per-layer) are f16 instead of q4_1.  They could be converted to q4_1,
  and the impact of the loss of precision would probably be low, but
  this would rule out exactly matching the Python implementation's
  output for validation.

- There is no sharding, since the input doesn't have it, and for a
  CPU-only implementation it seems more useful to avoid having to deal
  with multiple files.

The new format is differentiated from existing q4_1 format by changing
the 'f16' header flag to a new value, 4.  That said, I think a cleaner
approach would be to change main.cpp to support loading each tensor with
an arbitrary sharding configuration and type rather than hardcoding
specific combinations of types.  So far I've wasted too much time
debugging to try implementing this...

* Add missing permutation.  Now it works.

---------

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

2 Commits (436e56193199a1625f8c561069f702e8840a9e08)