* style: format with nixfmt/rfc101-style

* build(nix): Package gguf-py

* build(nix): Refactor to new scope for gguf-py

* build(nix): Exclude gguf-py from devShells

* build(nix): Refactor gguf-py derivation to take in exact deps

* build(nix): Enable pytestCheckHook and pythonImportsCheck for gguf-py

* build(python): Package python scripts with pyproject.toml

* chore: Cleanup

* dev(nix): Break up python/C devShells

* build(python): Relax pytorch version constraint

Nix has an older version

* chore: Move cmake to nativeBuildInputs for devShell

* fmt: Reconcile formatting with rebase

* style: nix fmt

* cleanup: Remove unncessary __init__.py

* chore: Suggestions from review

- Filter out non-source files from llama-scripts flake derivation
- Clean up unused closure
- Remove scripts devShell

* revert: Bad changes

* dev: Simplify devShells, restore the -extra devShell

* build(nix): Add pyyaml for gguf-py

* chore: Remove some unused bindings

* dev: Add tiktoken to -extra devShells

* server : remove multitask from server_task

* refactor completions handler

* fix embeddings

* use res_ok everywhere

* small change for handle_slots_action

* use unordered_set everywhere

* (try) fix test

* no more "mutable" lambda

* Apply suggestions from code review

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

* use deque

---------

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

…nov#9249)

* llama-bench : add JSONL (NDJSON) output mode

* llama-bench : update usage docs

Flake lock file updates:

• Updated input 'flake-parts':
    'github:hercules-ci/flake-parts/8471fe90ad337a8074e957b69ca4d0089218391d?narHash=sha256-XOQkdLafnb/p9ij77byFQjDf5m5QYl9b2REiVClC%2Bx4%3D' (2024-08-01)
  → 'github:hercules-ci/flake-parts/af510d4a62d071ea13925ce41c95e3dec816c01d?narHash=sha256-ODYRm8zHfLTH3soTFWE452ydPYz2iTvr9T8ftDMUQ3E%3D' (2024-08-30)
• Updated input 'nixpkgs':
    'github:NixOS/nixpkgs/c374d94f1536013ca8e92341b540eba4c22f9c62?narHash=sha256-Z/ELQhrSd7bMzTO8r7NZgi9g5emh%2BaRKoCdaAv5fiO0%3D' (2024-08-21)
  → 'github:NixOS/nixpkgs/71e91c409d1e654808b2621f28a327acfdad8dc2?narHash=sha256-GnR7/ibgIH1vhoy8cYdmXE6iyZqKqFxQSVkFgosBh6w%3D' (2024-08-28)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

* rpc : make RPC servers come first in the device list

* rpc : disable options for non-RPC builds

* rpc : rpc_count always zero for non-RPC builds

Fixed dmmv dequant for ncols== GGML_SYCL_DMMV_X

* Add AVX2 based implementations for quantize_q8_0_4x8, ggml_gemv_q4_0_8x8_q8_0 and ggml_gemm_q4_0_8x8_q8_0 functions

* Update code to fix issues occuring due to non alignment of elements to be processed as multiple of 16 in MSVC

* Update comments and indentation

* Make updates to reduce number of load instructions

)

build rpc-server for windows cuda

…8151)

* ggml-quants : 1.625 bpw ternary packing for BitNet 1.58b

* ggml-quants : faster 1.625 bpw AVX2 vec_dot

Not using a lookup table anymore makes it match q4_0 speed.

* gguf-py : fix formatting

* llama : remove spaces on empty line

* ggml-quants : subtract 1 when back in epi8

This makes the 1.625 bpw type go faster than q4_0. Still not the fastest.

* ggml-quants : Q2_2 now faster than Q4_K on with AVX2

* ggml-quants : cleanup Q1_3 code formatting

* ggml-quants : ARM NEON vec_dot for q2_2 and q1_3

* ggml-quants : use ceiling division when quantizing q1_3

* convert-hf : simplify BitNet pre-quantization

This still results in the exact same tensor weights and scales,
but it reveals some weirdness in the current algorithm.

* convert-hf : allow converting the weird BitNet 1.3B

Its FFN size is 5460 which is not convenient.
The offending tensors are kept in F16,
which makes the final model 5.01 bpw.

* bitnet : replace 1.58b with b1.58, as in the paper

* ggml-quants : fix build failure on Windows

* ggml-quants : attempt to fix Arm 32-bit support

* ggml : add some informative comments in q1_3 vec_dot

* ggml : add TQ1_0 and TQ2_0 ternary quantization types

* ggml : even faster TQ2_0

* ggml : also faster TQ1_0

Same optimization as for TQ2_0 by offsetting the sum instead of the weights.
This makes TQ1_0 almost as fast as Q8_0 on AVX2.

* ggml : fix build issues in certain environments

* ggml : add NEON vec_dot implementation for TQ1_0 and TQ2_0

* ggml : avoid directly using vmlal_high_s8, for 32-bit ARM compat

The compiler seems smart enough to use the same instruction
even when using vget_high_s8 instead.

* ggml : remove q1_3 and q2_2

No more 1.625 bpw and 2.000 bpw,
now instead using 1.6875 bpw and 2.0625 bpw
with TQ1_0 and TQ2_0, respectively.

* llama : remove the separate scale tensors of BitNet b1.58

They won't be needed, since the remaining ternary quant types have
built-in scales.

* ggml-quants : rename fields of TQ1_0 and TQ2_0 structs for consistency

* ggml-quants : allow using vdotq_s32 in TQ2_0 vec_dot

Not yet tested on hardware which supports it,
might not work or might not even compile. But also it might.
It should make the performance better on recent ARM CPUs.

* ggml-quants : remove comment about possible format change of TQ2_0

Making it slightly more convenient for AVX512
but less convenient for everything else is not worth the trouble.

* gguf-py : Numpy (de)quantization for TQ1_0 and TQ2_0

* ggml-quants : use roundf instead of nearest_int for TQ1_0 and TQ2_0

This does not change anything for ternary models,
since their values should never end up being in halfway cases anyway.

* convert : allow direct conversion to TQ1_0 and TQ2_0

The token embeddings and output tensors are kept in F16
to allow quantizing them to Q4_K and Q6_K with llama-quantize.

* llama : handle fallback for TQ1_0 and TQ2_0 with Q4_0

Q4_0 is not completely symmetric (so not lossless for ternary models),
but it should be good enough.

* ggml-quants : allow using ARM dot product instructions for TQ1_0

* ggml-quants : deduplicate TQ1_0 and TQ2_0 __ARM_FEATURE_DOTPROD support

* ggml : remove unused ggml_mul special case

It would otherwise conflict with the more general
optimization coming with Mamba-2.

* ggml : handle TQ1_0 and TQ2_0 in dequantization-based operators

* test-backend-ops : add TQ1_0 and TQ2_0 comments for later

Not yet adding uncommented, because some backends like SYCL and Metal
do not properly handle unknown types in supports_op for GGML_OP_MUL_MAT.
(and Metal also doesn't handle it with GGML_OP_GET_ROWS)
Support for TQ1_0 and TQ2_0 for other backends than CPU
will be added in follow-up pull requests.

* Improve Vulkan shader builds system

- Add dependency to vulkan-shaders-gen to rebuild shaders when changing the shader compilation utility.
- Add option to generate debug info for Vulkan shaders to provide shader source to Vulkan shader profiling tools

* remove not required self dependency

- windows build : Ok.
- linux build : Ok.

Signed-off-by: Changyeon Kim <cyzero.kim@samsung.com>

`--output-format` is modeled after `llama-bench`'s options

* llama-bench : add optional progress messages

* server : simplify state machine for slot

* add SLOT_STATE_DONE_PROMPT

* pop_deferred_task

* add missing notify_one

* fix passkey test

* metrics : add n_busy_slots_per_decode

* fix test step

* add test

* maybe fix AddressSanitizer?

* fix deque ?

* missing lock

* pop_deferred_task: also notify

* Update examples/server/server.cpp

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

---------

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

* ggml : fix missing cpu_set_t on emscripten

* better version

* bring back android part

- Add `struct llama_sampler` and `struct llama_sampler_i`
- Add `llama_sampler_` API
- Add `llama_sampler_chain_` API for chaining multiple samplers
- Remove `LLAMA_API_INTERNAL`
- Add `llama_perf_` API and remove old `llama_print_timings` and `llama_reset_timings`

* (wip) argparser v3

* migrated

* add test

* handle env

* fix linux build

* add export-docs example

* fix build (2)

* skip build test-arg-parser on windows

* update server docs

* bring back missing --alias

* bring back --n-predict

* clarify test-arg-parser

* small correction

* add comments

* fix args with 2 values

* refine example-specific args

* no more lamba capture

Co-authored-by: slaren@users.noreply.github.com

* params.sparams

* optimize more

* export-docs --> gen-docs

* common : do not add null tokens during warmup

ggml-ci

* llama : check that the input tokens are valid

ggml-ci

* tests : fix batch size of bert model

ggml-ci

…/934)

* ggml_cont: fix issue with transposed tensors when one dimension is 1

when using multiple threads, it is not enough
to check for the tensors to be contiguous for
ggml_compute_forward_dup_same_cont to work correctly.
The tensors strides also need to match.

Signed-off-by: Salvatore Mesoraca <s.mesoraca16@gmail.com>

* Add ggml_cont tests

Signed-off-by: Salvatore Mesoraca <s.mesoraca16@gmail.com>

* Remove dead code

it isn't possible to reach this code because
all these functions are invoked by ggml_compute_forward_dup
if and only if src0->type != dst->type

Signed-off-by: Salvatore Mesoraca <s.mesoraca16@gmail.com>

* Make ggml_compute_forward_dup_same_cont work with contiguous tensors

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
Signed-off-by: Salvatore Mesoraca <s.mesoraca16@gmail.com>

---------

Signed-off-by: Salvatore Mesoraca <s.mesoraca16@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

* enable Ascend NPU in src/whisper.cpp
  * sync test-backend-ops with llama.cpp

* tests: add gradient checking to test-backend-ops

* remove old comment

* reorder includes

* adjust SIN/COS parameters

* add documentation, use supports_op if possible

test-backend-ops fails because ggml_cont aborts
when invoked passing an unsupported type.

This commit makes ggml_cont tests pass

Signed-off-by: Salvatore Mesoraca <s.mesoraca16@gmail.com>

sin and cos failed test-backend-ops because they
tried to dereference a context pointer that is null
on dry runs.

This commit prevents that segfault.

Signed-off-by: Salvatore Mesoraca <s.mesoraca16@gmail.com>