* initial commit for sealion support
* add sealion support
* minor fix
* q/k ln and pos_embd only if required
* Apply suggestions from code review
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* minor : clear whitespaces
---------
Co-authored-by: bryan <bryansiow@aisingapore.org>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Add openchat chat template
* Add chat template test for openchat
* Add chat template for vicuna
* Add chat template for orca-vicuna
* Add EOS for vicuna templates
* Combine vicuna chat templates
* Add tests for openchat and vicuna chat templates
* Add chat template for alpaca
* Add separate template name for vicuna-orca
* Remove alpaca, match deepseek with jinja output
* Regenerate chat template test with add_generation_prompt
* Separate deepseek bos from system message
* Match openchat template with jinja output
* Remove BOS token from templates, unprefix openchat
* ggml : update mul_mat_id to use the same tensor for all the experts
* update cuda
* minor
* update metal
* update test-backend-ops
* fix cuda
* Update ggml-metal.m
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* update convert.py
* update convert-hf-to-gguf.py
* update convert.py for mixtral hf models
* Update convert-hf-to-gguf.py
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* cuda : support non-pow-2 number of experts
* allow quantize to work for split and merged experts models in the same way
* cleanup + disable mmap automatically with split tensors models
* update imatrix
* test-backend-ops : test qwen argsort
* update grok model loading
* llama : add merged experts tensors to the grok tensor map
* minor
* gguf : bump version
* fix quantizing of merged experts
* convert-hf-to-gguf.py : update grok (untested)
* make linter happy
* cuda/argsort : use shared memory instead of pool memory
* convert : fix grok tensor names
* metal : add support for non-pow-2 argsort
* llama : more loader cleanup, better error checking
* cuda : fix warning
* llama : still use mmap for loading old models, but copy the data to a host buffer
* add review note
* llama : remove ffn tensor counting + add sanity check
ggml-ci
* convert : fix handling of n_experts == None
ggml-ci
* imatrix : fix ncall counters
* llama : produce error if imatrix size does not match
* quantize : terminate on errors + trace logs
ggml-ci
* metal : pad shared memory to 16 bytes
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Fix Vulkan no kv offload incoherence
* Add k-quant mul mat mat shaders
* Rework working buffer allocation, reduces vram use noticeably
Clean up cpu assist code, replaced with ggml-backend offload function
* Default to all dedicated GPUs
* Add fallback for integrated GPUs if no dedicated GPUs are found
* Add debug info which device is allocating memory
* Fix Intel dequant issue
Fix validation issue
* Fix Vulkan GGML_OP_GET_ROWS implementation
* Clean up merge artifacts
* Remove Vulkan warning
* Support xverse model convert to gguf format.
* 1. Convert xverse models to gguf;
2. Add LLM_ARCH_XVERSE inference in llama.cpp;
3. Add xverse item in Supported models in README.md;
* * gguf-py: remove redundant logs
* llama: remove the init_mapping_prefetch custom parameter
* llama.cpp: Include the changes from #6122 to exclude the unused outputs of the last layers.
* - Fix format issues
- Remove duplicate set kqv_out to llm_build_kv
* Update llama.cpp
---------
Co-authored-by: willhe <willhe@xverse.cn>
Co-authored-by: willhe <hexin@xverse.cn>
* llama: remove redundant reshape in build_kv_store
This commit removes the reshape of the V matrix in the build_kv_store.
The motivation for this is that V matrix has the shape:
```console
(gdb) p *v_cur
$46 = {type = GGML_TYPE_F32, backend = GGML_BACKEND_TYPE_CPU,
buffer = 0x0, ne = {4096, 512, 1, 1}, nb = {4, 16384, 8388608,
8388608}, op = GGML_OP_MUL_MAT, op_params = {
0 <repeats 16 times>}, flags = 0, grad = 0x0,
src = {0xb496b0, 0x7ffef1c40950, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0, 0x0}, perf_runs = 0, perf_cycles = 0, perf_time_us = 0,
view_src = 0x0, view_offs = 0, data = 0x0,
name = "Vcur-0", '\000' <repeats 57 times>, extra = 0x0,
padding = "\000\000\000\000\000\000\000"}
```
And after reshaping this tensor we get:
```console
gdb) p *ggml_reshape_2d(ctx, v_cur, n_embd_v_gqa, n_tokens)
$44 = {type = GGML_TYPE_F32, backend = GGML_BACKEND_TYPE_CPU,
buffer = 0x0, ne = {4096, 512, 1, 1}, nb = {4, 16384, 8388608,
8388608}, op = GGML_OP_RESHAPE, op_params = {
0 <repeats 16 times>}, flags = 0, grad = 0x0,
src = {0x7ffef1c40e00, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0,
0x0}, perf_runs = 0, perf_cycles = 0, perf_time_us = 0,
view_src = 0x7ffef1c40e00, view_offs = 0, data = 0x0,
name = "Vcur-0 (reshaped)", '\000' <repeats 46 times>, extra = 0x0,
padding = "\000\000\000\000\000\000\000"}
```
I noticed that the `src` and `view_src` fields are different but that the
dimensions are the same. From the code comment it seems like the reshape
call is not needed and perhaps the above can motivate the removal of the
reshape call.
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
* llama : add assert
---------
Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Allow conversion of Mistral HF models
* Homogenize Llama, Mistral, Mixtral under the same entry.
* Fix tokenizer, permute tensors
* Use sentencepiece tokenizer, or fall back to hfft.
* convert-hf : small fix for mypy
* convert-hf : fix duplicated block_count
* convert-hf : add vocab size to metadata
---------
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
