* Adding q6_0_r4
We get PP-512(LLaMA-3.1-8B) = 257 t/s on a Ryzen-7950X.
* q6_0_r4: NEON
We get PP-512(LLaMA-3.1-8B) = 95 t/s on M2-Max.
In terms of ops, q6_0_r4 is identical to q5_0_r4
except for loading the high bits being
vld1q_u8_x2 instead of vld1q_u8. It is strange that
this can make a 5% difference in performance, especially
considering that this is amortized (re-used) over 8 columns
in the right matrix. Or am I running out of vector registers?
* Fix AVX2
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Adding q5_0_r4
We get PP-512(LLaMA-3.1-8B) = 256.7 t/s on a Ryzen-7950X.
We even get TG-128 improvement to 11.7 t/s from 11.1 t/s.
* q5_0_r4: NEON
We get PP-512(LLaMA-3.1-8B) = 99.6 t/s on M2-Max,
up from 71.0 t/s for Q5_0. The difference to mainline llama.cpp
is no longer funny: they get 26.5 t/s for Q5_0.
For TG, we are nor able to fully saturate memory bandwidth
and arrive at 22.1 t/s @ 8 threads. Mainline llama.cpp gets
20.6 t/s for Q5_0.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Adding q8_0_r4
We get PP-512(LLaMA-3.1-8B) = 268 t/s on a Ryzen-7950X compared
to 175.6 t/s for Q8_0.
* q8_0_r4: NEON
We get PP-512(LLaMA-3.1-8B) = 112.6 t/s on M2-Max.
* q8_0_r4: Zen4 matrix-vector specialization
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Adding iq4_0_r4 - q4_0 repacked
We get PP-512(LLaMA-3.1-8B) = 278 t/s on a Ryzen-7950X CPU,
so ~5-6% faster than iq4_nl_x4.
* q4_0_r4: NEON
Here we get 115.8 t/s, so also ~5% better than iq4_nl_x4.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Adding iq4_nl_x4
Looks very promising - I get PP-512(LLaMA-3.1-8B) = 230 t/s
on the Ryzen-7950X! This is faster than any other quant and
~40% faster than iq4_nl.
* iq4_nl_x4: getting amazing
This Zen4 variant gets us to PP-512(LLaMA-3.1-8B) = 263 t/s!
* iq4_nl_x4: AVX2
Here we gain only 25% compared to iq4_nl
* iq4_nl_x4: NEON
On M2-Max we get PP-512(LLaMA-3.1-8B) = 109.7 t/s, up from
82.4 t/s for iq4_nl.
* iq4_nl_x4: minor NEON improvement and cleanup
This gets us to 110.3 t/s. In comparison,
IQ4_NL_4_4 in mainline llama.cpp achieves 92.3 t/s.
* iq4_nl_x4: NEON specialization for matrix x vector
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* 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 f2d315b46f7aacc7df4b86bd8acba387b30e11ca.
As far as I can tell, the commit breaks Metal TG.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
To complement the token_embd.weight and output.weight :
attn_v.weight
attn_k.weight.
attn_q_weight
attn_output.weight
attn_qkv.weight
ffn_gate
ffn_down
ffn_up
* iq4_kss: WIP
* iq4_kss: CUDA dequantize works
So we can run perplexity. Sadly, the result does not look good
on the bpw vs quantization error plot.
* iq4_kss: slightly better quantization
* iq4_kss: another small quantization improvement
* iq4_kss: CUDA works
TG-128 performance is very decent with 131 t/s for LLaMA-3.1-8B.
In comparison, we have 123 t/s for q4_0 and 128 t/s for iq4_ks.
I.e., the reduced model size more than offsets the additional
bit fiddling required for iq4_kss.
* iq4_kss: new bit arrangement - CUDA and Zen4 work
Did not lose performance on CUDA. Zen4 is decent, but not great:
PP-512(LLaMA-3.1-8B) = 163 t/s.
TG-128 is of course better than other 4-bit quants due to smaller model size.
We get 14.5 t/s @ 8 threads.
* iq4_kss: ARM_NEON. Predictably very slow
* iq4_kss: Metal
PP is not too bad - just 10% slower than q4_0.
But TG is 30% slower, i.e., predictably bad.
* iq4_kss: somewhat faster Metal dot product
45.75 t/s -> 48.75 t/s.
Still 22% slower than q4_0
* iq4_kss: AVX2
Bad, but better than I expected.
PP-512(LLaMA-3.1-8B) = 167 t/s on the Ryzen-5950X.
I.e., with 32 AVX2 threads we get the performance of
16 Zen4 threads.
* iq4_kss: very slightly faster Metal dot product
48.7 t/s -> 49.3 t/s
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* iq4_k_xxs: basics
* WIP + adding iq3_kl quantization mix
* iq4_xxs: this looks very viable compared to iq4_xs
At the same 4.25 bpw PPL is always better, for some models
significantly better. I'll rename to iq4_ks and keep it.
* iq4_xxs: CUDA dot product
We get TG-128 = 126 t/s for LLaMA-3.1-8B, compared to 123 t/s for q4_0.
* iq4_xxs: scalar CPU dot product
Also fix the breakage I caused with the dedicated work buffer
quantization portion when the multiplication is not done
via iqk_mul_mat.
* iq4_xxs: Zen4
I noticed that iq4_xs is wrong on Zen4 (and possibly AVX2).
Again the same mistake of packing int32_t back to int16_t,
which overflows occasionally (just occasionally, that's why the
result doesn't look completely wrong, so I didn't notice).
* Fix iq4_xs (Zen4)
* iq4_xxs: AVX2
* iq4_xxs: ARM_NEON
* iq4_xxs: Metal
* iq4_xxs: slightly faster TG on Metal
* iq4_xxs: rename to iq4_ks
After all, tt is a smaller variant of iq4_k.
* iq3_kl: use iq4_ks instead of iq4_k/iq4_xs
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Adding q6_0 - basics + AVX2/Zen4 working
* Adding q6_0: CUDA dequantize works, but not mmvq
* Adding q6_0: CUDA mmvq works
* Adding q6_0: CUDA cpy, so Q6_0 can be used for KV-cache
* Add q6_0 to CPU flash attention
Disappointing result: for LlaMA-3.2-1B, q6_0 K- and V-cache
gives about the same PPL as q8_0 K-cache and q4_0 V-cache,
while needing the exact same RAM.
I.e., what was the point?
* q6_0: slightly better kv-cache result
Better than q8_0+q4_0, but not as good as q8_0+iq4_nl
* q6_0: works on ARM_NEON
* q6_0: dequantize works on Metal, but not vector dot product
* q6_0: it now works on Metal
Outperforms q5_0 by a significant margin. E.g.
| model | size | params | backend | ngl | threads | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ------: | ------------: | ---------------: |
| llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 44.02 ± 0.08 |
| llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | tg128 | 40.13 ± 0.12 |
| llama 8B Q6_0 | 6.08 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 500.55 ± 0.32 |
| llama 8B Q5_0 | 5.21 GiB | 8.03 B | Metal | 100 | 4 | pp512 | 448.02 ± 0.27 |
* q6_0: can now be used for kv-cache on Metal
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* POC: per row scale
This is a POC how to work around opinionated ggml to
have scales per row rather than per block.
Only implemened for Zen4 and only for iq2_tn.
* POC per row scale: iq2_tn on NEON
* POC per row scale: iq2_tn on Metal
* Per row scale Metal templates
* iq1_tn: shrink to 1.625 bpw (NEON and Metal)
* POC per row scale: CUDA
* POC per row scale: add CUDA TODOs
There are two places in ggml-cuda.cu left where it is assumed
that type_size * n_per_row / block_size is the way to compute
and handle row sizes. This does not affect simple usage,
but will lead to issues when tensors are split between GPUs.
* Per row scales - CUDA
The only place left where there are unnecessary assumptions being made
is in the Flash Attention code. As we are not using any quants that
use per row scales for quantized KV cache, it should be OK for now.
* Update IQ1_TN and IQ2_TN bpw shown to user
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Adding iq1_tn - 1.6875 bpw for TriLM ternary models
* iq1_tn: NEON
* iq1_tn: faster NEON
* iq2_bn: improve performance on NEON
We now get TG-128 = 100 t/s for Bitnet-3B-1.58b!
* iq1_tn: improve AVX2
PP-512 goes to 533 t/s up from 455.
TG-128 @ 2 threads goes to 16.6 t/s up from 14.2.
However, we seem to have a bottleneck somewhere as
TG saturates at 8 threads.
* iq1_tn: improve Zen4
PP-512 goes to 485 t/s up from 352. With FA we get 545 t/s up from 380.
TG-128 @ 1 thread goes to 12.4 t/s up from 10.4.
However, we seem to have a bottleneck somewhere as
TG saturates at 8 threads.
* iq2_bn: improve on Zen4
We now get PP-512 = 614 t/s up from 542 t/s
* iq2_bn: improve AVX2 implementation
We now get PP-512 = 753 t/s up from 680 t/s.
* Remove unnecessary barrier in ggml_compute_forward_mul_mat
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Zen4 Flash Attnetion: WIP bf16
* Zen4 Flash Attnetion: bf16 seems to be working
* Zen4 Flash Attnetion: improving bf16
* Zen4 Flash Attnetion: improving bf16
It is better (slightly faster) to first convert Q
to bf16 before processing each block of q_step rows.
This requires D*q_step*sizeof(bf16) bytes, so at
most 4 kb for the head sizes we support, so we can
just allocate on the stack instead of reserving and
passing a work buffer in ggml.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Softcap: WIP
Fuses scale + tanh + scale as used for softcaping in some
models.
Just CPU for now. ~1.4% for PP-512 on Gemma2-9b, no effect on TG.
Somewhat surprisingly the improvement does not increase as I
go to longer contexts. Gemma2 does softcap on K*Q, which grows
quadratically with context length, so I would have thought
the benefit from fusing scale, tanh, scale would increase.
But no, no luck.
* softcap: CUDA
* softcap: CUDA
~1% speedup for Gemma2-9b
* softcap: Metal and NEON
About 1% speedup.
* Simdified gelu
Gives ~1% speedup for Gemma2-9b prompt processing on AVX512/AVX2.
It looks like the gelu operation is memory bound on my CPU's
after SIMD-ifying it. By not using the 128 kb gelu lookup table
we gain a small advantage.
On the M2-Max the lookup table is slightly faster than the SIMD
version, so left the lookup table for ARM_NEON.
* softcap, tanh: avoid NaNs for large arguments (AVX2, AVX512)
Not that I have encountered this in practice, but just to be sure.
This does it for AVX512 and AVX2, still need a guard for ARM_NEON.
* llama-bench: add ability to turn off warmup runs
So we don't need to wait forever on, e.g., benchmarks involving
long contexts.
* softcap, tanh: avoid NaNs for large arguments (NEON)
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
This allows for a better comparison between different models
or different tensors of the same model where the magnitude of
the model weights may differ.
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* iq2_tn: TriLM specific 2.0625 bpw quantization
Quantize/dequantize/scale dot product.
I get 46 t/s for the TriLM-3.9B with any SIMD!
Finally a compiler doing a decent job auto-vectorizing the
scalar implementation.
* iq2_tn: AVX512
Just reusing the k-quants template gets us to PP-512 = 376 t/s,
TG-128 = 47.6 t/s for TriLM-3.9B.
* iq2_tn: AVX512
With this tweak we get to PP-512 = 431 t/s.
* iq2_tn: AVX512
With this tweak we get TG-128 = 19.58 / 35.18 t/s for 1 / 2 threads.
At 4 threads we saturate at 48.41 t/s, and then performance slowly
degrades with increasing number of threads.
* iq2_tn: AVX2
PP512 = 440 t/s on the Ryzen-5975WX.
We should be able to do better.
* iq2_tn: initial NEON version
* iq2_tn: NEON
For TriLM-3.9B running on the M2-Max we get PP-512 = 193.5 t/s,
TG-128 = 75.5 t/s. This is in line with what we have for
iq2_bn ant 3.3B Bitnet.
* iq2_tn: Metal
For TriLM-3.9B on a 30-core M2-Max we get PP-512 = 890 t/s,
TG-128 = 98.5 t/s.
* iq2_tn: CUDA
For TriLM-3.9B running on RTX-4080 we get PP-512 = 9936 t/s,
TG-128 = 299.2 t/s.
* iq2_tn: AVX2 PP improvement
We now get PP-512 = 490.73 t/s for TriLM-3.9B on the Ryzen-5975WX.
We have PP-512 = 636.61 t/s for Bintnet-3B quantized with iq2_bn.
Bintnet-3B is actually 3.4B, TriLM-3.9B is 3.99B, so we would
expect 3.43/3.99 * 636 = 546 t/s, so it seems we still have something
that is not quite optimal in iq2_tn.
* iq2_tn: small NEON improvement
For TriLM-3.9B we now get PP-512 = 206.6 t/s and TG-128 = 76.4 t/s.
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* iq4_k: basics
* quantize/dequantize works
* CUDA dequantize works and one can run PPL calcs. I get
PPL = 6.5258 for LlaMA-3.1-8B, which is 1.77% above fp16.
In comparison, q4_K_S (same size) is 2.88% above fp16.
* TG on CUDA does not work. Johannes has changed the way i-quant dot
products are done, so need to sort out what he had in mind
* iqk_mul_mat is not implemented.
* iq4_k: TG now works on CUDA
* iq4_k: AVX512 implementation
For LLaMA-3.1-8B we get PP-512 = 182.6 t/s, TG-128 = 13.6 t/s,
so almost the same as q4_K_S.
* iq4_k: AVX2 implementation
For LLaMA-3.1-8B we get PP-512 = 203.1 t/s, TG-128 = 12.9 t/s
on the Ryzen-5975X.
* iq4_k: NEON implementation
For LLaMA-3.1-8B we get PP-512 = 60.7 t/s, TG-128 = 25.0 t/s
on the M2-Max. TG is on par with q4_K_S, PP is ~10% slower.
* iq4_k: Metal implementation
For LLaMA-3.1-8B we get PP-512 = 445 t/s, TG-128 = 46.3 t/s
on a 30-core M2-Max GPU. This is to be compared with (currently)
PP-512 = 460 t/s, TG-128 = 51 t/s for q4_K_S.
* iq4_k: scalar dot product
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Merging mainline - WIP
* Merging mainline - WIP
AVX2 and CUDA appear to work.
CUDA performance seems slightly (~1-2%) lower as it is so often
the case with llama.cpp/ggml after some "improvements" have been made.
* Merging mainline - fix Metal
* Remove check
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
For some models the same tensor is used for token embeddings and
output. This tensor tends to be named token_embedding.weight rather
than output.weight, which prevernts us from collecting imatrix data
for this tensor. With this commit we can tell the name of the
output tensor to the imatrix tool.
* create append_pooling operation; allow to specify attention_type; add last token pooling; update examples
* find result_norm/result_embd tensors properly; update output allocation logic
* only use embd output for pooling_type NONE
* get rid of old causal_attn accessor
* take out attention_type; add in llama_set_embeddings
* bypass logits when doing non-NONE pooling
* cuda sqrt support
* enable cuda in pca
* fix comments in pca
* add test
* add sqrt to ggml_backend_cuda_supports_op
* fix test
* new line
* Use F32 sqrtf instead of F64 sqrt
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
---------
Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
* add control-vector-generator
* calc diff
* add comments
* proof-of-concept stdlib implementation
Implements PCA and file writing using mostly standard libraries. The output is recognized as a functional control vector, but outputs gibberish.
* param parsing, refactor, comments
Added basic command-line parameters for outfile and one each positive/negative prompt.
Refactored some messy code in PCA computation and GGUF exporting.
Left a bunch of comments regarding further work needed.
* example template completions
Implements an example template set built from the positive/negative prompts like the control vector Python implementation.
* add multi prompts, multi-thread for PCA
* fix mem error
* add debugs
* fix matrix transpose multiplication
you have got to be kidding me
* preliminary template/multiprompt support
model is running out of context and that ought to be fixed (segfaulting) but other than that it looks goodish
* fix zero output & param parsing, functional templating
fixed a bug where the output file had no tensor data/was all zero
fixed a bug where single hyphen flags were not being correctly parsed
implements creation of templated prompts from input (still need to adapt based on model)
* fix square_diff matmul index range and CRLF->LF line endings
fixed a logic error where square_diff would not multiply all rows
fixed a formatting error where the provided completions.txt had CRLF line endings
* add command-line args for num threads, num completions file lines, always reload model
refactored a few things and did what the commit message says on the tin
* code aestheticization
* fix compiler warnings
* in-series multithreading for prompt embedding?
added commented-out code to attempt to start implementing mutlithreading for embedding in main
* remove unnecessary multithreading
* interim fix memory leak
* translated everything but PCA (I think)
* tentatively translate the rest
* fix ggml errors and make new ones
at least it compiles and runs
* fix cb_eval
* temporary commit while I move dev environments
it finally outputs a functioning control vector - "functioning" in the sense that it can be loaded and it clearly has the right idea, but makes the model incoherent
* update debug statements
* pre-tokenize so we can allocate correct memory to ctx_diffs_wrapped
* update comments
* (wip) refactor
* clean up PCA ggml implementation
* fix shape of v_diff_original
* add n_batch for pca
* working version
* remember to copy back the last_eigenvector
* fix n_completions
* bring back n_completions
* default n_pca_batch to 20
* fix macos build
* add to makefile all targets
* use ggml_format_name
* add readme
* fix .editorconfig
* use ggml_backend_tensor_copy
* attemp to fix compile problem on mac
* fix compile warn
* reuse allocr
* move param parser to common
* better error handling
* clean up a bit
* add print_usage
* shorten help msg
* beautify help msg
* escape prompt by default
* change compile target to llama-cvector-generator
* typo
* disable GPU for PCA
* code style
---------
Co-authored-by: Christian Zhou-Zheng <christianzhouzheng@gmail.com>
* move BLAS to a separate backend
* rename GGML_USE_OPENBLAS to GGML_USE_BLAS
* alloc : reuse same buffer when the same buffer type if used multiple times
* set number of threads automatically for openblas and blis
* sched : print assignments when GGML_SCHED_DEBUG env variable is set
* sched : allow ops with weights on an incompatible buffer type
This will cause the weight to be copied to a backend that supports the
op, which is very costly. The weight should have been stored in a buffer
of a backend that can run the op, but llama.cpp cannot do this
automatically at the moment.
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* server : Smart selection of available slot using Longest Common Substring
* add usage
* remove trailing whitespaces
* Use Longest Common Prefix (LCP) instead of LCS
* Rename argument
* avoid to get prompt in infill mode and embedding mode
* remove embedding mode
* refactor format
---------
Co-authored-by: wudexiang <wudexiang@bytedance.com>
* common : gpt_params_parse do not print usage
* common : rework usage print (wip)
* common : valign
* common : rework print_usage
* infill : remove cfg support
* common : reorder args
* server : deduplicate parameters
ggml-ci
* common : add missing header
ggml-ci
* common : remote --random-prompt usages
ggml-ci
* examples : migrate to gpt_params
ggml-ci
* batched-bench : migrate to gpt_params
* retrieval : migrate to gpt_params
* common : change defaults for escape and n_ctx
* common : remove chatml and instruct params
ggml-ci
* common : passkey use gpt_params
* ic
* migrate my eary work
* add the belonging stuff: css,favicon etc
* de prompts
* chore: Update HTML meta tags in index.html file
* add api-key css classes
* some necessary fixes
* Add API key CSS classes and update styling in style.css
* clean the code
* move API to the top, rearrange param sliders. update css
* add tooltips to the parameters with comprehensible explanations
* fix FloatField and BoolField tooltips
* fix grammar field width
* use template literales for promptFormats.js
* update const ModelGenerationInfo
* remove ms per token, since not relevant for most webui users and use cases
* add phi-3 prompt template
* add phi3 to dropdown
* add css class
* update forgotten css theme
* add user message suffix
* fix chatml & add llama3 format
* fix llama3 prompt template
* more prompt format fixes
* add more comon stop tokens
* add missing char
* do not separate with new line or comma
* move prompt style
* add hacky llama2 prompt solution, reduce redundancy in promptFormats.js
* fix toggle state localstorage
* add cmd-r prompt et reduce redundancy
* set default prompt to empty
* move files, clean code
* fix css path
* add a button to the new ui
* move new ui to "/public" due to otherwise problematic CORS behaviour
* include new ui in cpp
* fix wrong link to old ui
* renaming to ensure consistency
* fix typos "prompt-format" -> "prompt-formats"
* use correct indent
* add new ui files to makefile
* fix typo
* SimpleChat:DU:BringIn local helper js modules using importmap
Use it to bring in a simple trim garbage at end logic, which is
used to trim received response.
Also given that importmap assumes esm / standard js modules, so
also global variables arent implicitly available outside the
modules. So add it has a member of document for now
* SimpleChat:DU: Add trim garbage at end in loop helper
* SimpleChat:DU:TrimGarbage if unable try skip char and retry
* SimpleChat:DU: Try trim using histogram based info
TODO: May have to add max number of uniq chars in histogram at
end of learning phase.
* SimpleChat:DU: Switch trim garbage hist based to maxUniq simple
Instead of blindly building histogram for specified substring
length, and then checking if any new char within specified min
garbage length limit, NOW exit learn state when specified maxUniq
chars are found. Inturn there should be no new chars with in
the specified min garbage length required limit.
TODO: Need to track char classes like alphabets, numerals and
special/other chars.
* SimpleChat:DU: Bring in maxType to the mix along with maxUniq
Allow for more uniq chars, but then ensure that a given type of
char ie numerals or alphabets or other types dont cross the
specified maxType limit. This allows intermixed text garbage
to be identified and trimmed.
* SimpleChat:DU: Cleanup debug log messages
* SimpleChat:UI: Move html ui base helpers into its own module
* SimpleChat:DU:Avoid setting frequence/Presence penalty
Some models like llama3 found to try to be over intelligent by
repeating garbage still, but by tweaking the garbage a bit so that
it is not exactly same. So avoid setting these penalties and let
the model's default behaviour work out, as is.
Also the simple minded histogram based garbage trimming from end,
works to an extent, when the garbage is more predictable and
repeatative.
* SimpleChat:UI: Add and use a para-create-append helper
Also update the config params dump to indicate that now one needs
to use document to get hold of gMe global object, this is bcas of
moving to module type js.
Also add ui.mjs to importmap
* SimpleChat:UI: Helper to create bool button and use it wrt settings
* SimpleChat:UI: Add Select helper and use it wrt ChatHistoryInCtxt
* SimpleChat:UI:Select: dict-name-value, value wrt default, change
Take a dict/object of name-value pairs instead of just names.
Inturn specify the actual value wrt default, rather than the
string representing that value.
Trap the needed change event rather than click wrt select.
* SimpleChat:UI: Add Div wrapped label+element helpers
Move settings related elements to use the new div wrapped ones.
* SimpleChat:UI:Add settings button and bring in settings ui
* SimpleChat:UI:Settings make boolean button text show meaning
* SimpleChat: Update a bit wrt readme and notes in du
* SimpleChat: GarbageTrim enable/disable, show trimmed part ifany
* SimpleChat: highlight trim, garbage trimming bitmore aggressive
Make it easy for end user to identified the trimmed text.
Make garbage trimming logic, consider a longer repeat garbage
substring.
* SimpleChat: Cleanup a bit wrt Api end point related flow
Consolidate many of the Api end point related basic meta data into
ApiEP class.
Remove the hardcoded ApiEP/Mode settings from html+js, instead use
the generic select helper logic, inturn in the settings block.
Move helper to generate the appropriate request json string based
on ApiEP into SimpleChat class itself.
* SimpleChat:Move extracting assistant response to SimpleChat class
so also the trimming of garbage.
* SimpleChat:DU: Bring in both trim garbage logics to try trim
* SimpleChat: Cleanup readme a bit, add one more chathistory length
* SimpleChat:Stream:Initial handshake skeleton
Parse the got stream responses and try extract the data from it.
It allows for a part read to get a single data line or multiple
data line. Inturn extract the json body and inturn the delta
content/message in it.
* SimpleChat: Move handling oneshot mode server response
Move handling of the oneshot mode server response into SimpleChat.
Also add plumbing for moving multipart server response into same.
* SimpleChat: Move multi part server response handling in
* SimpleChat: Add MultiPart Response handling, common trimming
Add logic to call into multipart/stream server response handling.
Move trimming of garbage at the end into the common handle_response
helper.
Add new global flag to control between oneshot and multipart/stream
mode of fetching response. Allow same to be controlled by user.
If in multipart/stream mode, send the stream flag to the server.
* SimpleChat: show streamed generative text as it becomes available
Now that the extracting of streamed generated text is implemented,
add logic to show the same on the screen.
* SimpleChat:DU: Add NewLines helper class
To work with an array of new lines. Allow adding, appending,
shifting, ...
* SimpleChat:DU: Make NewLines shift more robust and flexible
* SimpleChat:HandleResponseMultiPart using NewLines helper
Make handle_response_multipart logic better and cleaner. Now it
allows for working with the situation, where the delta data line
got from server in stream mode, could be split up when recving,
but still the logic will handle it appropriately.
ALERT: Rather except (for now) for last data line wrt a request's
response.
* SimpleChat: Disable console debug by default by making it dummy
Parallely save a reference to the original func.
* SimpleChat:MultiPart/Stream flow cleanup
Dont try utf8-decode and newlines-add_append if no data to work on.
If there is no more data to get (ie done is set), then let NewLines
instance return line without newline at end, So that we dont miss
out on any last-data-line without newline kind of scenario.
Pass stream flag wrt utf-8 decode, so that if any multi-byte char
is only partly present in the passed buffer, it can be accounted
for along with subsequent buffer. At sametime, bcas of utf-8's
characteristics there shouldnt be any unaccounted bytes at end,
for valid block of utf8 data split across chunks, so not bothering
calling with stream set to false at end. LATER: Look at TextDecoder's
implementation, for any over intelligence, it may be doing..
If needed, one can use done flag to account wrt both cases.
* SimpleChat: Move baseUrl to Me and inturn gMe
This should allow easy updating of the base url at runtime by the
end user.
* SimpleChat:UI: Add input element helper
* SimpleChat: Add support for changing the base url
This ensures that if the user is running the server with a
different port or wants to try connect to server on a different
machine, then this can be used.
* SimpleChat: Move request headers into Me and gMe
Inturn allow Authorization to be sent, if not empty.
* SimpleChat: Rather need to use append to insert headers
* SimpleChat: Allow Authorization header to be set by end user
* SimpleChat:UI+: Return div and element wrt creatediv helpers
use it to set placeholder wrt Authorization header.
Also fix copy-paste oversight.
* SimpleChat: readme wrt authorization, maybe minimal openai testing
* SimpleChat: model request field for openai/equivalent compat
May help testing with openai/equivalent web services, if they
require this field.
* SimpleChat: readme stream-utf-8 trim-english deps, exception2error
* Readme: Add a entry for simplechat in the http server section
* SimpleChat:WIP:Collate internally, Stream mode Trap exceptions
This can help ensure that data fetched till that point, can be
made use of, rather than losing it.
On some platforms, the time taken wrt generating a long response,
may lead to the network connection being broken when it enters
some user-no-interaction related power saving mode.
* SimpleChat:theResp-origMsg: Undo a prev change to fix non trim
When the response handling was moved into SimpleChat, I had changed
a flow bit unnecessarily and carelessly, which resulted in the non
trim flow, missing out on retaining the ai assistant response.
This has been fixed now.
* SimpleChat: Save message internally in handle_response itself
This ensures that throwing the caught exception again for higher
up logic, doesnt lose the response collated till that time.
Go through theResp.assistant in catch block, just to keep simple
consistency wrt backtracing just in case.
Update the readme file.
* SimpleChat:Cleanup: Add spacing wrt shown req-options
* SimpleChat:UI: CreateDiv Divs map to GridX2 class
This allows the settings ui to be cleaner structured.
* SimpleChat: Show Non SettingsUI config field by default
* SimpleChat: Allow for multiline system prompt
Convert SystemPrompt into a textarea with 2 rows. Reduce
user-input-textarea to 2 rows from 3, so that overall
vertical space usage remains same.
Shorten usage messages a bit, cleanup to sync with settings ui.
* SimpleChat: Add basic skeleton for saving and loading chat
Inturn when ever a chat message (system/user/model) is added,
the chat will be saved into browser's localStorage.
* SimpleChat:ODS: Add a prefix to chatid wrt ondiskstorage key
* SimpleChat:ODS:WIP:TMP: Add UI to load previously saved chat
This is a temporary flow
* SimpleChat:ODS:Move restore/load saved chat btn setup to Me
This also allows being able to set the common system prompt
ui element to loaded chat's system prompt.
* SimpleChat:Readme updated wrt save and restore chat session info
* SimpleChat:Show chat session restore button, only if saved session
* SimpleChat: AutoCreate ChatRequestOptions settings to an extent
* SimpleChat: Update main README wrt usage with server
* SimpleChat: A placeholder system prompt, Use usage msg in code
Just have a alert msg wrt needing javascript enabled in html. And
have usage message from js file. Update the usage message a bit.
So also enable switch session wrt setup_ui call.
Add a possible system prompt as a placeholder for the system-input.
* SimpleChat:CompletionMode: Allow control of Role: prefix
* SimpleChat:Completion: Avoid Role: prefix; Newline only in between
In completion mode
* avoid inserting Role: prefix before each role's message
* avoid inserting newline at the begin and end of the prompt
message. However if there are multiple role messages, then
insert newline when going from one role's message to the
next role's message.
* SimpleChat:CompletionMode: Update readme/usage, trim textarea newline
Readme update wrt completion mode behavior.
Usage help updated wrt completion mode behavior.
When changing from input to textarea elment wrt user input, the last
newline at the end of the user input wrt textarea, was forgotten to be
filtered, this is fixed now. However if user wants to have a explicit
newline they can using shift+enter to insert a newline, that wont be
removed. The extra newline removal logic uses substring and keyup to
keep things simple and avoid some previously noted bugs wrt other
events in the key path as well as IME composition etal.
* SimpleChat:SC: Ensure proper clearing/reseting
previous logic would have cleared/reset the xchat, without doing
the same wrt iLastSys, thus leading to it pointing to a now non
existent role-content entry.
So if a user set a system prompt and used completion mode, it would
have done the half stupid clear, after the model response was got.
Inturn when user tries to send a new completion query, it would
inturn lead to handle_user_submit trying to add/update system prompt
if any, which will fail, bcas iLastSys will be still pointing to a
non existant entry.
This is fixed now, by having a proper clear helper wrt SC class.
* SimpleChat: Update usage note and readme a bit
* SimpleChat:Completion: clear any prev chat history at begining
Previously any chat history including model response to a completion
query would have got cleared, after showing the same to the user,
at the end of handle_user_submit, rather than at the begining.
This gave the flexibility that user could switch from chat mode
to completion mode and have the chat history till then sent to
the ai model, as part of the completion query. However this flow
also had the issue that, if user switches between different chat
sessions, after getting a completion response, they can no longer
see the completion query and its response that they had just got.
The new flow changes the clearing of chat history wrt completion
mode to the begining of handle_user_submit, so that user doesnt
lose the last completion mode query and response, till a new
completion mode query is sent to the model, even if they were to
switch between the chat sessions. At the same time the loss of
flexibility wrt converting previous chat history into being part
of the completion query implicitly doesnt matter, because now
the end user can enter multiline queries.
* SimpleChat:Try read json early, if available
For later
the server flow doesnt seem to be sending back data early, atleast
for the request (inc options) that is currently sent.
if able to read json data early on in future, as and when ai model
is generating data, then this helper needs to indirectly update
the chat div with the recieved data, without waiting for the
overall data to be available.
* SimpleChat: Rename the half asleep mis-spelled global var
* SimpleChat: Common chat request options from a global object
* SimpleChat: Update title, usage and readme a bit
Keep the title simple so that print file name doesnt have chars
that need to be removed.
Update readme wrt some of the new helpers and options.
Change Usage list to a list of lists, add few items and style it
to reduce the margin wrt lists.
* SimpleChat:ChatRequestOptions: max_tokens
As some times based on the query from the user, the ai model may get
into a run away kind of generation with repeatations etal, so adding
max_tokens to try and limit this run away behaviour, if possible.
* SimpleChat: Reduce max_tokens to be small but still sufficient
* SimpleChat: Consolidate global vars into gMe, Display to user
This allows the end user to see the settings used by the logic,
as well as allows users to change/update the settings if they
want to by using devel-tools/console
* SimpleChat:SlidingWindow: iRecentUserMsgCnt to limit context load
This is disabled by default. However if enabled, then in addition
to latest system message, only the last N user messages, after the
latest system message and its reponses from the ai model will be sent
to the ai-model, when querying for a new response.
This specified N also includes the latest user query.
* SimpleChat: placeholder based usage hint for user-in textarea
* SimpleChat: Try make user experience better, if possible
Reduce chat history context sent to the server/ai-model to be
just the system-prompt, prev-user-request-and-ai-response and
cur-user-request, instead of the previous full chat history.
This way if there is any response with garbage/repeatation, it
doesnt mess with things beyond the next question, in some ways.
Increase max_tokens to 1024, so that a relatively large previous
reponse doesnt eat up the space available wrt next query-response.
However dont forget that the server when started should also
be started with a model context size of 1k or more, to be on
safe side.
Add frequency and presence penalty fields set to 1.2 to the set
of fields sent to server along with the user query. So that
the model is partly set to try avoid repeating text in its
response.
* SimpleChat:Add n_predict (equiv max_tokens) for llamacpp server
The /completions endpoint of examples/server doesnt take max_tokens,
instead it takes the internal n_predict, for now add the same on
the client side, maybe later add max_tokens to /completions endpoint
handling.
* SimpleChat: Note about trying to keep things simple yet flexible
* main : don't print special tokens with --grammar
The CLI interface was recently changed to print special control tokens
like the </s> stop message one. This token shouldn't be printed if the
grammar flag was passed, unless the grammar specifies it, because that
breaks shell-scriptability.
* main: use seperate stream for control characters
* main: use dprintf and add --ctrl-token-no-out and --ctrl-token-fd-out
* main: dprintf isn't part of the IEEE POSIX standard. Just use write().
* main: remove --ctrl-token-fd-out in favor for fcntl() based detection
* common.cpp: accidentally removed --interactive-first
* main: only merge stdout and control token if not in conversation or grammar mode
* main: rejig control token descriptor handling
* main: must check pipe status on very top of program
* main: renamed --no-special from --ctrl-token-no-out and other refactoring
* main: refactor ctrl_token_no_out --> no_special
* llama: rename llama_token_is_control_token() to llama_token_is_control()
* main: remove special token file descriptor feature (#5)
---------
Co-authored-by: Brian <mofosyne@gmail.com>
* Make tokenizer.cpp CLI tool nicer.
Before this commit, tokenize was a simple CLI tool like this:
tokenize MODEL_FILENAME PROMPT [--ids]
This simple tool loads the model, takes the prompt, and shows the tokens
llama.cpp is interpreting.
This changeset makes the tokenize more sophisticated, and more useful
for debugging and troubleshooting:
tokenize [-m, --model MODEL_FILENAME]
[--ids]
[--stdin]
[--prompt]
[-f, --file]
[--no-bos]
[--log-disable]
It also behaves nicer on Windows now, interpreting and rendering Unicode
from command line arguments and pipes no matter what code page the user
has set on their terminal.
* style fix: strlen(str) == 0 --> *str == 0
* Simplify tokenize.cpp; by getting rid of handling positional style arguments.
It must now be invoked with long --model, --prompt etc. arguments only.
Shortens the code.
* tokenize.cpp: iostream header no longer required
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Co-authored-by: brian khuu <mofosyne@gmail.com>
* SimpleChat: Add a skeletal html page
Contains a div placeholder for showing chat messages till now
a text-input for allowing user to enter next chat message/query
to the model.
a submit button to allow sending of the user entered message and
chat till now to the model.
* SimpleChat: A js skeleton with SimpleChat class
Allows maintaining an array of chat message.
Allows adding chat message (from any of the roles be it system,
user, assistant, ...)
Allows showing chat messages till now, in a given div element.
* SimpleChat: request_json, globals, startme
* SimpleChatJS: Roles Class, submitClick
Define Role class with static members corresponding to the roles.
Update startme to
* Get hold of the ui elements.
* Attach a click handler to submit button, which adds the user input
to xchats array and shows the chat messages till now in chat div
element.
Trap DOMContentLoaded to trigger startme
* SimpleChat:HTML: Bring in the js file
* SimpleChat: Rather value wrt input text element
* SimpleChat: Also add completions related prompt
* SimpleChat: Use common helper logic wrt json data
* SimpleChat: Move handling of submit request into its own func
* SimpleChat: Try handshake with llm over its web service endpoint
* SimpleChat:JS: Extract model response and show to user
* SimpleChat:JS: Messages/Prompt, indicate working to end user
* SimpleChat: Try keep input element in view
* SimpleChat: Diff user/assistant msgs, Make input wider
Also show a default message to user
Also add some metas
* SimpleChat: Move into its own sub directory to avoid confusion
* SimpleChat:sh: Add simple shell script to run python3 http.server
So one needs to run the llm server locally
then run this script and access it using a local browser
* SimpleChat:JS: Try trap enter key press wrt input text field
So user can either press submit button or press enter key
* SimpleChat: Allow user to select chat or completion mode
* SimpleChat: Dont submit if already submitted and waiting
Also make chat the default selection wrt mode
* SimpleChat:JS: Handle difference in response
Try read the assistance response from appropriate field in the
response got.
Also examples/server seems to return the response in a slightly
different field, so try account for that also.
* SimpleChat:JS: Force completion mode be single message by default
* SimpleChat: Add a simple readme file
* SimpleChat:HTML: Cleanup/structure UI a bit, Add input for system
* SimpleChat:Allow system prompt to be set, if provided before user
* SimpleChat: Ignore empty user input, without trimming
* SimpleChat:Alert user if they provide sysprompt late or change it
* SimpleChat: Move handling systemprompt into its own func
* SimpleChat:HTML: Add a style for system role message
* SimpleChat: Update the readme file
* SimpleChat:CSS: Move style info into its own css file
To keep it simple, clean and seperate so that things are not
unnecessarily cluttered.
* SimpleChat:CSS: Allow for chat div to be scrollable
* SimpleChat:JS: Try ensure the last entry in chat is visible
Needed because now only the chat div is scrollable and not the full
page.
In last commit the chat div size was fixed to 75% vertical height,
so the full page no longer scrolls, so the old bring user-input
element to view wont work, instead now the last element in the
chat div should be brought into view.
* SimpleChat:JS: bottom of element visible, Set focus to user input
As the generated text could be multiple lines and occupy more space
that the full scrollable div's vertical space, make the bottom of
the last element (which can be such a generated text) in the div
visible by scrolling.
Ensure that the user input box has focus
* SimpleChat: Update notes a bit. Try keep browser happy
Avoid browser quirk mode with DOCTYPE.
Help with accessibility a bit by specifying the language explicitly.
Specify the char encoding explicitly, inturn utf-8 is a safe bet,
even with intermixing of languages if reqd in future.
Add a cache-control http-equiv meta tag, which in all probability
will be ignored.
Defer js loading and execution, just for fun and future, not that
critical here as it stands now.
* SimpleChat:HTML:Group user input+btn together; Note about multichat
* SimpleChat:JS: Allow for changing system prompt anytime for future
* SimpleChat:Readme: Note about handle_systemprompt begin/anytime
* SimpleChat:HTML: Add viewport meta for better mobile friendliness
Without this the page content may look too small.
* SimpleChat:HtmlCss: Cleanup UI flow
set margin wrt vmin rather than vw or vh so portrait/landscape ok.
Use flex and flex-grow to put things on the same line as well as
distribute available space as needed. Given two main elements/line
so it remains simple.
In each line have one element with grows and one sits with a basic
comfortably fixed size.
* SimpleChat: textarea for multiline user chat, inturn shift+enter 4 enter
* SimpleChat: Make vertical layout better responsive (flex based)
Also needed to make things cleaner and properly usable whether
landscape or portrait, after changing to multiline textarea rather
than single line user input.
Avoid hardcoding the chat-till-now display area height, instead
make it a flex-growable within a flex column of ui elements within
a fixed vertical area.
* SimpleChat: Rename simplechat.html to index.html, update readme
Instead of providing a seperate shell script, update the readme wrt
how to run/use this web front end.
* SimpleChat: Screen fixed view and scrolling, Printing full
* SimpleChat:JS:CI: Avoid space at end of jsdoc param line
* SimpleChat:JS: MultiChat initial skeleton
Will help maintain multiple independent chats in future
* SimpleChat:JS: Move system prompt begin/anytime into SimpleChat
* SimpleChat:JS:Keep MultiChatUI simple for now
Worry about different chats with different servers for later.
* SimpleChat:JS: Move handle submit into MultiChat, build on same
Create an instance of MultiChatUI and inturn a instance of chat
session, which is what the UI will inturn work on.
* SimpleChat:JS: Move to dictionary of SimpleChat, instead of array
* SimpleChat: Move ui elements into MultiChatUI, Update el IDs
Move ui elements into MultiChatUI, so that current handleUserSubmit
doesnt need to take the element arguments. Also in future, when
user is allowed to switch between different chat sessions, the
UI can be updated as needed by using the elements in UI already
known to MultiChatUI instance.
Rename the element ids' so that they follow a common convention,
as well as one can identify what the element represents in a more
consistant manner.
* SimpleChat:MCUI:Show available chat sessions, try switch btw them
Previous commits brought in / consolidated existing logic into
MultiChatUI class.
Now start adding logic towards multichat support
* show buttons indicating available chat sessions
* on sessin button click, try switch to that session
* SimpleChat:MCUI: Store and use current chat session id
Also
allow to switch chat session optionally, wrt some of the related
helpers.
setup for two chat sessions by default.
* SimpleChat:MCUI: Delay enabling user-input to avoid race
Re-enable user-input, only after response to a user query has been
updated to the chat-div. This ensures that if user tries to switch
chat session, it wont be allowed till chat-request-response flow is
done.
* SimpleChat: Take care of system prompt
Helper to get the latest system prompt and inturn use same to
set the system prompt ui, when switching.
Ensure that system prompt is set if and when enter key is pressed.
* SimpleChat:GetSystemLatest, fix a oversight.
* SimpleChat:MCUI: Allow selected chat-session btn to be highlighted
Also have a general helper for setting class of children.
* SimpleChat:Cleanup corners
Show system prompt in chat space, when it is set by pressing enter,
as a feedback to user.
Alert user, if they try to switch chat session in the middle of
waiting for a response from the ai model.
* SimpleChat:MCUI: Ensure req-resp failure doesnt lock up things
* SimpleChat:MCUI: Support for new chat sessions
Also a general create button helper.
* SimpleChat:MCUI: CreateSessionBtn helper, use wrt NewChat
Also fix a oversight wrt using stale data wrt the list of chat
sessions.
* SimpleChat:MCUI: NewChat btn first before existing chat sessions
* SimpleChat:MCUI:CornerCases:Skip new chat, show only if current
Skip NewChat if user cancels or if one waiting for response from
the ai model.
Dont show a chat with newly got ai model response, if current chat
session has changed, some how. Chat session shouldnt be allowed to
change, if there is a pending response, but still as a additional
sanity check.
* SimpleChat: Update readme, title, show usage if no chat to show
* SimpleChat: Cleanup the log/dialog messages a bit
* phi3 : duplicate rope factors in each layer
phi3 : set phi-3 model type as 14B
model loader : simplify the process for duplicating model tensors
llama-bench : remove default pg test
* replace bool parameters in llama_model_loader with named flags
* add phi3 128k support in convert-hf-to-gguf
* add phi3 128k support in cuda
* address build warnings on llama.cpp
* adjust index value in cuda long rope freq factors
* add long rope support in ggml cpu backend
* make freq factors only depend on ctx size
* remove unused rope scaling type 'su' frin gguf converter
* fix flint warnings on convert-hf-to-gguf.py
* set to the short freq factor when context size is small than trained context size
* add one line of comments
* metal : support rope freq_factors
* ggml : update ggml_rope_ext API to support freq. factors
* backends : add dev messages to support rope freq. factors
* minor : style
* tests : update to use new rope API
* backends : fix pragma semicolons
* minor : cleanup
* llama : move rope factors from KV header to tensors
* llama : remove tmp assert
* cuda : fix compile warning
* convert : read/write n_head_kv
* llama : fix uninitialized tensors
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* examples: cache hf model when --model not provided
* examples: cache hf model when --model not provided
* examples: cache hf model when --model not provided
* examples: cache hf model when --model not provided
* examples: cache hf model when --model not provided
* Update brute force test: add_special
* Update brute force test: default values for add_bos_token and add_eos_token
* Enable rtrim when pre-inserting BOS
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Revert "server : fix test regexes"
* Update brute force test: special tokens
* Fix added tokens
- Try to read 'added_tokens.json'.
- Try to read 'tokenizer_config.json'.
- Try to read 'tokenizer.json'.
* Fix special tokens rtrim
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* server : fix test regexes
* android : use "ci-android" branch for CI
* ggml : disable SIMD exp and silu for 32-bit ARM
ggml-ci
* android : do not fetch, use add_subdirectory instead
* cmake : provide binary dir
Kawrakow
Nexes the Elder
Kawrakow
Douglas Hanley
Shuichi Tsutsumi
luoyu-intel
sasha0552
Sigbjørn Skjæret
Calvin Laurenson
Xuan Son Nguyen
Radoslav Gerganov
slaren
Olivier Chafik
Georgi Gerganov
Johannes Gäßler
mgroeber9110
Christian Zhou-Zheng
woodx
arch-btw
nickp27
Yazan Agha-Schrader
HanishKVC
Galunid
Nathan Epstein
Ikko Eltociear Ashimine
Brian
Justine Tunney
Elton Kola
Mikko Juola
Neo Zhang
liuwei-git
Amir
jaime-m-p
Fred Douglas