* set default n_batch to 512 when using BLAS
* spacing
* alternate implementation of setting different n_batch for BLAS
* set n_batch to 512 for all cases
* ggml : prefer vzip to vuzp
This way we always use the same type of instruction across all quantizations
* ggml : alternative Q4_3 implementation using modified Q8_0
* ggml : fix Q4_3 scalar imlpementation
* ggml : slight improvement of Q4_3 - no need for loop unrolling
* ggml : fix AVX paths for Q8_0 quantization
* Moving parameters to separate lines for readability.
* Increasing repeate_penalty to 1.1 to make alpaca more usable by default.
* Adding trailing newline.
* reserve correct size for logits
* add functions to get and set the whole llama state:
including rng, logits, embedding and kv_cache
* remove unused variables
* remove trailing whitespace
* fix comment
* Improve cuBLAS performance by using a memory pool
* Move cuda specific definitions to ggml-cuda.h/cu
* Add CXX flags to nvcc
* Change memory pool synchronization mechanism to a spin lock
General code cleanup
* A faster version for Q4_1 x Q8_0 dot products
The idea nehind being that Q8_0 quantized
values get used many times in the matrix multiplications
where they are involved. In the current implementations,
when we are evaluating the dot products, we need to compute
the sum of the quants in the Q8_0 vector, so the same
operation is repeated many times. Here we pre-compute
the sum during Q8_0 quantization, store it in the
now modified block_q8_0 struct, and then reuse this
result in the subsequent dot products.
In a synthetic benchmark (just compute a bunch of dot
products), this change speeds up the Q4_1 * Q8_0 dot
product by 80%, making the performance identical to
Q4_0 * Q8_0.
In practical application, I see a ~15% gain in speed for
token prediction on M2, and ~5% gain on Ryzen 7950X.
The speed gain in the prompt evaluation is much bigger
(around 50%).
I have only done the change for the scalar version,
ARM_NEON, and AVX2, so we still need an AVX implementation.
* Cleaning up
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Multi-threading quantization.
Not much gain for simple quantizations, bit it will be important
for quantizations that require more CPU cycles.
* Multi-threading for quantize-stats
It now does the job in ~14 seconds on my Mac for
Q4_0, Q4_1 and Q4_2. Single-threaded it was taking
more than 2 minutes after adding the more elaborate
version of Q4_2.
* Reviewer comments
* Avoiding compiler confusion
After changing chunk_size to const int as suggested by
@ggerganov, clang and GCC starting to warn me that I don't
need to capture it in the lambda. So, I removed it from the
capture list. But that makes the MSVC build fail. So,
making it a constexpr to make every compiler happy.
* Still fighting with lambda captures in MSVC
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
[Accelerate](https://developer.apple.com/documentation/accelerate) is an Apple framework which can only be used on macOS, and the CMake build [ignores](https://github.com/ggerganov/llama.cpp/blob/master/CMakeLists.txt#L102) the `LLAMA_ACCELERATE` variable when run on non-Apple platforms. This implies setting `LLAMA_ACCELERATE` is a no-op on Ubuntu and can be removed.
This will reduce visual noise in CI check results (in addition to reducing the number of checks we have to run for every PR). Right now every sanitized build is duplicated twice for no good reason (e.g., we have `CI / ubuntu-latest-cmake-sanitizer (ADDRESS, Debug, ON)` and `CI / ubuntu-latest-cmake-sanitizer (ADDRESS, Debug, OFF)`).
* Q4_2 quantization with rmse-optimized scale and quants
For quantize-stats we get
q4_2: rmse 0.00159301, maxerr 0.17480469, 95pct<0.0030, median<0.0012
For 7B perplexity with BLAS enabled we get 6.2038 after 655 chunks.
Quantization is slow (~90 seconds on my Mac for 7B) as not
multi-threaded as in PR #896.
* ggml : satisfy the sanitizer builds
Not sure why this makes them fail
* Better follow ggml conventions for function names
* Fixed type as per reviewer comment
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
On my Mac, the direct Q4_1 product is marginally slower
(~69 vs ~55 us for Q4_0). The SIMD-ified ggml version
is now almost 2X slower (~121 us).
On a Ryzen 7950X CPU, the direct product for Q4_1 quantization
is faster than the AVX2 implementation (~60 vs ~62 us).
---------
Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
* Replaced static initialization of complex objects with a initialization on first use. This prevents an undefined behavior on program run, for example, crash in Release build, works in Debug build
* replaced use of auto with exact type to avoid using -std=c++14
* Made the assessors functions for static maps be static const
