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Merge branch 'master' into compilade/refactor-kv-cache

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This commit is contained in:
Francis Couture-Harpin 2024-07-04 17:33:52 -04:00
commit 9b38f8bf65
102 changed files with 4745 additions and 1675 deletions
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17
.devops/nix/package.nix
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@ -17,19 +17,18 @@
rocmPackages,
vulkan-headers,
vulkan-loader,
clblast,
curl,
useBlas ? builtins.all (x: !x) [
useCuda
useMetalKit
useOpenCL
useRocm
useVulkan
] && blas.meta.available,
useCuda ? config.cudaSupport,
useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL,
useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin,
useMpi ? false, # Increases the runtime closure size by ~700M
useOpenCL ? false,
useRocm ? config.rocmSupport,
enableCurl ? true,
useVulkan ? false,
llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
@ -56,7 +55,6 @@ let
++ lib.optionals useCuda [ "CUDA" ]
++ lib.optionals useMetalKit [ "MetalKit" ]
++ lib.optionals useMpi [ "MPI" ]
++ lib.optionals useOpenCL [ "OpenCL" ]
++ lib.optionals useRocm [ "ROCm" ]
++ lib.optionals useVulkan [ "Vulkan" ];
@ -198,19 +196,19 @@ effectiveStdenv.mkDerivation (
optionals effectiveStdenv.isDarwin darwinBuildInputs
++ optionals useCuda cudaBuildInputs
++ optionals useMpi [ mpi ]
++ optionals useOpenCL [ clblast ]
++ optionals useRocm rocmBuildInputs
++ optionals useBlas [ blas ]
++ optionals useVulkan vulkanBuildInputs;
++ optionals useVulkan vulkanBuildInputs
++ optionals enableCurl [ curl ];
cmakeFlags =
[
(cmakeBool "LLAMA_BUILD_SERVER" true)
(cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
(cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
(cmakeBool "LLAMA_CURL" enableCurl)
(cmakeBool "GGML_NATIVE" false)
(cmakeBool "GGML_BLAS" useBlas)
(cmakeBool "GGML_CLBLAST" useOpenCL)
(cmakeBool "GGML_CUDA" useCuda)
(cmakeBool "GGML_HIPBLAS" useRocm)
(cmakeBool "GGML_METAL" useMetalKit)
@ -254,7 +252,6 @@ effectiveStdenv.mkDerivation (
useCuda
useMetalKit
useMpi
useOpenCL
useRocm
useVulkan
;
@ -281,7 +278,7 @@ effectiveStdenv.mkDerivation (
# Configurations we don't want even the CI to evaluate. Results in the
# "unsupported platform" messages. This is mostly a no-op, because
# cudaPackages would've refused to evaluate anyway.
badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin;
badPlatforms = optionals useCuda lib.platforms.darwin;
# Configurations that are known to result in build failures. Can be
# overridden by importing Nixpkgs with `allowBroken = true`.

2
.github/ISSUE_TEMPLATE/config.yml vendored
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@ -9,5 +9,3 @@ contact_links:
- name: Want to contribute?
url: https://github.com/ggerganov/llama.cpp/wiki/contribute
about: Head to the contribution guide page of the wiki for areas you can help with

11
.gitignore vendored
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@ -98,13 +98,14 @@ examples/server/*.mjs.hpp
# Python
__pycache__
.venv
/Pipfile
dist
poetry.lock
/.venv
__pycache__/
*/poetry.lock
poetry.toml
# Nix
/result
# Test binaries
/tests/test-backend-ops
/tests/test-double-float

6
CMakeLists.txt
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@ -42,6 +42,10 @@ endif()
option(BUILD_SHARED_LIBS "build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
if (WIN32)
add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
endif()
#
# option list
#
@ -152,7 +156,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake
DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama)
install(
FILES convert-hf-to-gguf.py
FILES convert_hf_to_gguf.py
PERMISSIONS
OWNER_READ
OWNER_WRITE

1
CMakePresets.json
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@ -19,6 +19,7 @@
"cacheVariables": {
"CMAKE_EXPORT_COMPILE_COMMANDS": "ON",
"CMAKE_CXX_COMPILER": "icx",
"CMAKE_C_COMPILER": "cl",
"GGML_SYCL": "ON",
"CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
}

6
Makefile
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@ -62,6 +62,11 @@ TEST_TARGETS = \
tests/test-tokenizer-1-bpe \
tests/test-tokenizer-1-spm
# Legacy build targets that were renamed in #7809, but should still be removed when the project is cleaned
LEGACY_TARGETS = main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama \
retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm
# Deprecation aliases
ifdef LLAMA_CUBLAS
$(error LLAMA_CUBLAS is removed. Use GGML_CUDA instead.)
@ -1086,6 +1091,7 @@ clean:
rm -vrf ggml/src/ggml-cuda/template-instances/*.o
rm -rvf $(BUILD_TARGETS)
rm -rvf $(TEST_TARGETS)
rm -rvf $(LEGACY_TARGETS)
find examples pocs -type f -name "*.o" -delete
#

6
README.md
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@ -108,6 +108,7 @@ Typically finetunes of the base models below are supported as well.
- [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon)
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
- [X] [BERT](https://github.com/ggerganov/llama.cpp/pull/5423)
- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
- [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft)
- [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila)
@ -217,6 +218,11 @@ Unless otherwise noted these projects are open-source with permissive licensing:
**Tools:**
- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML
- [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
**Infrastructure:**
- [Paddler](https://github.com/distantmagic/paddler) - Stateful load balancer custom-tailored for llama.cpp
---

2
ci/run.sh
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@ -688,7 +688,7 @@ function gg_run_embd_bge_small {
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
model_f16="${path_models}/ggml-model-f16.gguf"
model_q8_0="${path_models}/ggml-model-q8_0.gguf"

21
common/common.cpp
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@ -757,7 +757,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa
params.cache_type_v = argv[++i];
return true;
}
if (arg == "--multiline-input") {
if (arg == "-mli" || arg == "--multiline-input") {
params.multiline_input = true;
return true;
}
@ -2070,7 +2070,24 @@ std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_par
if (params.warmup) {
LOG("warming up the model with an empty run\n");
std::vector<llama_token> tmp = { llama_token_bos(model), llama_token_eos(model), };
std::vector<llama_token> tmp;
llama_token bos = llama_token_bos(model);
llama_token eos = llama_token_eos(model);
// some models (e.g. T5) don't have a BOS token
if (bos != -1) {
tmp.push_back(bos);
}
tmp.push_back(eos);
if (llama_model_has_encoder(model)) {
llama_encode(lctx, llama_batch_get_one(tmp.data(), tmp.size(), 0, 0));
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == -1) {
decoder_start_token_id = bos;
}
tmp.clear();
tmp.push_back(decoder_start_token_id);
}
llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0));
llama_past_clear(lctx);
llama_synchronize(lctx);

1
common/common.h
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@ -459,4 +459,3 @@ void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const cha
void yaml_dump_non_result_info(
FILE * stream, const gpt_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);

352
convert-hf-to-gguf.py → convert_hf_to_gguf.py
View File

@ -13,7 +13,7 @@ import sys
from enum import IntEnum
from pathlib import Path
from hashlib import sha256
from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast
from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Literal, Sequence, TypeVar, cast
import math
import numpy as np
@ -490,6 +490,9 @@ class Model:
if chkhsh == "7fc505bd3104ca1083b150b17d088b59534ede9bde81f0dd2090967d7fe52cee":
# ref: https://huggingface.co/LumiOpen/Viking-7B
res = "viking"
if chkhsh == "b53802fb28e26d645c3a310b34bfe07da813026ec7c7716883404d5e0f8b1901":
# ref: https://huggingface.co/core42/jais-13b
res = "jais"
if res is None:
logger.warning("\n")
@ -576,7 +579,19 @@ class Model:
special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
special_vocab.add_to_gguf(self.gguf_writer)
def _set_vocab_sentencepiece(self):
def _set_vocab_sentencepiece(self, add_to_gguf=True):
tokens, scores, toktypes = self._create_vocab_sentencepiece()
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
def _create_vocab_sentencepiece(self):
from sentencepiece import SentencePieceProcessor
tokenizer_path = self.dir_model / 'tokenizer.model'
@ -638,14 +653,7 @@ class Model:
scores.append(-1000.0)
toktypes.append(SentencePieceTokenTypes.UNUSED)
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
return tokens, scores, toktypes
def _set_vocab_llama_hf(self):
vocab = gguf.LlamaHfVocab(self.dir_model)
@ -669,6 +677,51 @@ class Model:
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
def _set_vocab_builtin(self, model_name: Literal["gpt-neox", "llama-spm"], vocab_size: int):
tokenizer_path = Path(sys.path[0]) / "models" / f"ggml-vocab-{model_name}.gguf"
logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
vocab_reader = gguf.GGUFReader(tokenizer_path, "r")
default_pre = "mpt" if model_name == "gpt-neox" else "default"
field = vocab_reader.get_field(gguf.Keys.Tokenizer.MODEL)
assert field # tokenizer model
self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8"))
field = vocab_reader.get_field(gguf.Keys.Tokenizer.PRE)
self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else default_pre)
field = vocab_reader.get_field(gguf.Keys.Tokenizer.LIST)
assert field # token list
self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
if model_name == "llama-spm":
field = vocab_reader.get_field(gguf.Keys.Tokenizer.SCORES)
assert field # token scores
self.gguf_writer.add_token_scores([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
field = vocab_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
assert field # token types
self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
if model_name != "llama-spm":
field = vocab_reader.get_field(gguf.Keys.Tokenizer.MERGES)
assert field # token merges
self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)) is not None:
self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)) is not None:
self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)) is not None:
self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)) is not None:
self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0])
if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_BOS)) is not None:
self.gguf_writer.add_add_bos_token(field.parts[-1].tolist()[0])
if (field := vocab_reader.get_field(gguf.Keys.Tokenizer.ADD_EOS)) is not None:
self.gguf_writer.add_add_eos_token(field.parts[-1].tolist()[0])
@Model.register("GPTNeoXForCausalLM")
class GPTNeoXModel(Model):
@ -1934,7 +1987,7 @@ class Phi3MiniModel(Model):
if len(rope_scaling_type) == 0:
raise KeyError('Missing the required key rope_scaling.type')
if rope_scaling_type == 'su':
if rope_scaling_type == 'su' or rope_scaling_type == 'longrope':
attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0
elif rope_scaling_type == 'yarn':
attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0
@ -2308,6 +2361,8 @@ class GemmaModel(Model):
special_vocab._set_special_token("eot", 107)
special_vocab.add_to_gguf(self.gguf_writer)
self.gguf_writer.add_add_space_prefix(False)
def set_gguf_parameters(self):
hparams = self.hparams
block_count = hparams["num_hidden_layers"]
@ -2345,7 +2400,20 @@ class Gemma2Model(Model):
model_arch = gguf.MODEL_ARCH.GEMMA2
def set_vocab(self):
self._set_vocab_llama_hf()
tokens, scores, toktypes = self._create_vocab_sentencepiece()
# hack: This is required so that we can properly use start/end-of-turn for chat template
for i in range(108):
# including <unusedX>, <start_of_turn>, <end_of_turn>
toktypes[i] = SentencePieceTokenTypes.CONTROL
self.gguf_writer.add_tokenizer_model("llama")
self.gguf_writer.add_tokenizer_pre("default")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_scores(scores)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
special_vocab.add_to_gguf(self.gguf_writer)
self.gguf_writer.add_add_space_prefix(False)
def set_gguf_parameters(self):
@ -2369,6 +2437,12 @@ class Gemma2Model(Model):
self.gguf_writer.add_final_logit_softcapping(
self.hparams["final_logit_softcapping"]
)
self.gguf_writer.add_sliding_window(self.hparams["sliding_window"])
# sanity check
attn_scalar = self.hparams["query_pre_attn_scalar"]
if attn_scalar != hparams["hidden_size"] / hparams["num_attention_heads"]:
raise ValueError("query_pre_attn_scalar must be equal to n_embd / n_head")
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unusem
@ -2410,39 +2484,7 @@ class MambaModel(Model):
self._set_vocab_sentencepiece()
else:
# Use the GPT-NeoX tokenizer when no tokenizer files are present
tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf"
logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
neox_reader = gguf.GGUFReader(tokenizer_path, "r")
field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8") if field else "gpt2")
field = neox_reader.get_field(gguf.Keys.Tokenizer.PRE)
self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else "mpt")
field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
assert field
self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
assert field
self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
assert field
self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0] if field else 1)
field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0] if field else 0)
field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0] if field else 0)
field = neox_reader.get_field(gguf.Keys.Tokenizer.PAD_ID)
self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0] if field else 0)
self._set_vocab_builtin("gpt-neox", vocab_size)
def set_gguf_parameters(self):
d_model = self.find_hparam(["hidden_size", "d_model"])
@ -2723,6 +2765,82 @@ class JinaBertV2Model(BertModel):
self.gguf_writer.add_add_eos_token(True)
@Model.register("OpenELMForCausalLM")
class OpenELMModel(Model):
model_arch = gguf.MODEL_ARCH.OPENELM
@staticmethod
def _make_divisible(v: float | int, divisor: int) -> int:
# ref: https://huggingface.co/apple/OpenELM-270M-Instruct/blob/eb111ff2e6724348e5b905984063d4064d4bc579/configuration_openelm.py#L34-L38
new_v = max(divisor, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
ffn_multipliers: list[float] = self.hparams["ffn_multipliers"]
ffn_dim_divisor: int = self.hparams["ffn_dim_divisor"]
self._n_embd: int = self.hparams["model_dim"]
self._num_kv_heads: list[int] = self.hparams["num_kv_heads"]
self._num_query_heads: list[int] = self.hparams["num_query_heads"]
self._ffn_dims: list[int] = [
OpenELMModel._make_divisible(multiplier * self._n_embd, ffn_dim_divisor)
for multiplier in ffn_multipliers
]
assert isinstance(self._num_kv_heads, list) and isinstance(self._num_kv_heads[0], int)
assert isinstance(self._num_query_heads, list) and isinstance(self._num_query_heads[0], int)
# Uses the tokenizer from meta-llama/Llama-2-7b-hf
def set_vocab(self):
try:
self._set_vocab_sentencepiece()
except FileNotFoundError:
self._set_vocab_builtin("llama-spm", self.hparams["vocab_size"])
def set_gguf_parameters(self):
n_embd = self._n_embd
head_dim = self.hparams["head_dim"]
rot_pct = 1.0
assert self.block_count == len(self._num_kv_heads)
assert self.block_count == len(self._num_query_heads)
assert self.block_count == len(self._ffn_dims)
self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name)
self.gguf_writer.add_block_count(self.block_count)
self.gguf_writer.add_context_length(self.hparams["max_context_length"])
self.gguf_writer.add_embedding_length(n_embd)
self.gguf_writer.add_feed_forward_length(self._ffn_dims)
self.gguf_writer.add_head_count(self._num_query_heads)
self.gguf_writer.add_head_count_kv(self._num_kv_heads)
self.gguf_writer.add_rope_freq_base(self.hparams["rope_freq_constant"])
# https://huggingface.co/apple/OpenELM-270M-Instruct/blob/c401df2/modeling_openelm.py#L30
self.gguf_writer.add_layer_norm_rms_eps(1e-6)
self.gguf_writer.add_rope_dimension_count(int(rot_pct * head_dim))
self.gguf_writer.add_key_length(head_dim)
self.gguf_writer.add_value_length(head_dim)
self.gguf_writer.add_file_type(self.ftype)
def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any:
if "n_layers" in keys:
return self.hparams["num_transformer_layers"]
return super().find_hparam(keys, optional)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
# split ff
if bid is not None and name == f"transformer.layers.{bid}.ffn.proj_1.weight":
ff_dim = self._ffn_dims[bid]
yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim])
yield (self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:])
return
yield (self.map_tensor_name(name), data_torch)
@Model.register("ArcticForCausalLM")
class ArcticModel(Model):
model_arch = gguf.MODEL_ARCH.ARCTIC
@ -2953,11 +3071,17 @@ class DeepseekV2Model(Model):
raise ValueError(f"Unprocessed experts: {experts}")
@Model.register("T5ForConditionalGeneration")
@Model.register("T5WithLMHeadModel")
@Model.register("T5ForConditionalGeneration")
@Model.register("MT5ForConditionalGeneration")
@Model.register("UMT5ForConditionalGeneration")
class T5Model(Model):
model_arch = gguf.MODEL_ARCH.T5
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.shared_token_embeddings_found = False
def set_vocab(self):
# to avoid TypeError: Descriptors cannot be created directly
# exception when importing sentencepiece_model_pb2
@ -2965,17 +3089,29 @@ class T5Model(Model):
from sentencepiece import SentencePieceProcessor
from sentencepiece import sentencepiece_model_pb2 as model
tokenizer_path = self.dir_model / 'spiece.model'
tokenizer_path = self.dir_model / 'tokenizer.model'
# many older models use spiece.model tokenizer model filename
if not tokenizer_path.is_file():
tokenizer_path = self.dir_model / 'spiece.model'
if not tokenizer_path.is_file():
raise FileNotFoundError(f"File not found: {tokenizer_path}")
sentencepiece_model = model.ModelProto()
sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read())
# some models like Pile-T5 family use BPE tokenizer instead of Unigram
if sentencepiece_model.trainer_spec.model_type == 2: # BPE
# assure the tokenizer model file name is correct
assert tokenizer_path.name == 'tokenizer.model'
return self._set_vocab_sentencepiece()
else:
assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM
add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix
remove_whitespaces = sentencepiece_model.normalizer_spec.remove_extra_whitespaces
precompiled_charsmap = sentencepiece_model.normalizer_spec.precompiled_charsmap
assert sentencepiece_model.trainer_spec.model_type == 1 # UNIGRAM
tokenizer = SentencePieceProcessor()
tokenizer.LoadFromFile(str(tokenizer_path))
@ -3045,7 +3181,10 @@ class T5Model(Model):
def set_gguf_parameters(self):
self.gguf_writer.add_name("T5")
self.gguf_writer.add_context_length(self.hparams["n_positions"])
if (n_ctx := self.find_hparam(["n_positions"], optional=True)) is None:
logger.warning("Couldn't find context length in config.json, assuming default value of 512")
n_ctx = 512
self.gguf_writer.add_context_length(n_ctx)
self.gguf_writer.add_embedding_length(self.hparams["d_model"])
self.gguf_writer.add_feed_forward_length(self.hparams["d_ff"])
self.gguf_writer.add_block_count(self.hparams["num_layers"])
@ -3061,16 +3200,111 @@ class T5Model(Model):
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
# Sometimes T5 and Flan-T5 based models contain "encoder.embed_tokens.weight" tensor or
# "decoder.embed_tokens.weight" tensors that are duplicates of "shared.weight" tensor
# To prevent errors caused by an unnecessary unmapped tensor, skip both of them and use only "shared.weight".
if name == "decoder.embed_tokens.weight" or name == "encoder.embed_tokens.weight":
logger.debug(f"Skipping tensor {name!r} in safetensors so that convert can end normally.")
return []
# T5 based models contain shared token embeddings tensors saved randomly as either "encoder.embed_tokens.weight",
# "decoder.embed_tokens.weight" or "shared.weight" tensor. In some models there are even multiple of them stored
# in the safetensors files. We use the first tensor from these three as the token embeddings for both encoder
# and decoder and ignore the remaining ones.
if name in ["decoder.embed_tokens.weight", "encoder.embed_tokens.weight", "shared.weight"]:
if not self.shared_token_embeddings_found:
name = "shared.weight"
self.shared_token_embeddings_found = True
else:
logger.debug(f"Skipping shared tensor {name!r} in safetensors so that convert can end normally.")
return []
return [(self.map_tensor_name(name), data_torch)]
@Model.register("JAISLMHeadModel")
class JaisModel(Model):
model_arch = gguf.MODEL_ARCH.JAIS
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# SwigLU activation
assert self.hparams["activation_function"] == "swiglu"
# ALiBi position embedding
assert self.hparams["position_embedding_type"] == "alibi"
# Embeddings scale
self.embeddings_scale = 1.0
# note: For some JAIS flavors, output is tied to (same as) wte in original model
self.output_is_wte = False
if 'mup_embeddings_scale' in self.hparams:
self.output_is_wte = True # Hack (?)
self.embeddings_scale = self.hparams['mup_embeddings_scale']
elif 'embeddings_scale' in self.hparams:
self.embeddings_scale = self.hparams['embeddings_scale']
else:
assert False
self.width_scale = 1.0
if 'mup_output_alpha' in self.hparams:
assert 'mup_width_scale' in self.hparams
self.width_scale = self.hparams['mup_output_alpha'] * self.hparams['mup_width_scale']
elif 'width_scale' in self.hparams:
self.width_scale = self.hparams['width_scale']
else:
assert False
self.max_alibi_bias = 8.0
def set_vocab(self):
self._set_vocab_gpt2()
def set_gguf_parameters(self):
self.gguf_writer.add_name(self.dir_model.name)
self.gguf_writer.add_block_count(self.hparams["n_layer"])
self.gguf_writer.add_context_length(self.hparams["n_positions"])
self.gguf_writer.add_embedding_length(self.hparams["n_embd"])
self.gguf_writer.add_feed_forward_length(self.hparams["n_inner"])
self.gguf_writer.add_head_count(self.hparams["n_head"])
self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"])
self.gguf_writer.add_file_type(self.ftype)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
del bid # unused
tensors: list[tuple[str, Tensor]] = []
# we don't need these
if name.endswith((".attn.bias")):
return tensors
if name.endswith(("relative_pe.slopes")):
# Calculate max ALiBi bias (this is the inverse of the ALiBi calculation)
# Some other models has max_alibi_bias spelled out explicitly in the hyperparams,
# but Jais's PyTorch model simply precalculates the slope values and places them
# in relative_pes.slopes
n_head_closest_log2 = 2 ** math.floor(math.log2(self.hparams["n_head"]))
first_val = float(data_torch._data[0])
self.max_alibi_bias = -round(math.log2(first_val) * n_head_closest_log2)
return tensors
if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_fc2.weight")):
data_torch = data_torch.transpose(1, 0)
new_name = self.map_tensor_name(name)
if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD):
tensors.append((new_name, data_torch * self.embeddings_scale))
if self.output_is_wte:
tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch * self.width_scale))
elif new_name == self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT):
assert not self.output_is_wte
tensors.append((new_name, data_torch * self.width_scale))
else:
tensors.append((new_name, data_torch))
return tensors
def write_tensors(self):
super().write_tensors()
self.gguf_writer.add_max_alibi_bias(self.max_alibi_bias)
###### CONVERSION LOGIC ######
@ -3226,7 +3460,8 @@ def main() -> None:
"auto": gguf.LlamaFileType.GUESSED,
}
if args.use_temp_file and (args.split_max_tensors > 0 or args.split_max_size != "0"):
is_split = args.split_max_tensors > 0 or args.split_max_size != "0"
if args.use_temp_file and is_split:
logger.error("Error: Cannot use temp file when splitting")
sys.exit(1)
@ -3263,11 +3498,12 @@ def main() -> None:
if args.vocab_only:
logger.info("Exporting model vocab...")
model_instance.write_vocab()
logger.info("Model vocab successfully exported.")
logger.info(f"Model vocab successfully exported to {model_instance.fname_out}")
else:
logger.info("Exporting model...")
model_instance.write()
logger.info("Model successfully exported.")
out_path = f"{model_instance.fname_out.parent}{os.sep}" if is_split else model_instance.fname_out
logger.info(f"Model successfully exported to {out_path}")
if __name__ == '__main__':

25
convert-hf-to-gguf-update.py → convert_hf_to_gguf_update.py
View File

@ -45,6 +45,7 @@ class TOKENIZER_TYPE(IntEnum):
SPM = auto()
BPE = auto()
WPM = auto()
UGM = auto()
# TODO: this string has to exercise as much pre-tokenizer functionality as possible
@ -86,6 +87,10 @@ models = [
{"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
{"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
{"name": "viking", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
{"name": "gemma", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", },
{"name": "gemma-2", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
{"name": "jais", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
{"name": "t5", "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
]
@ -107,9 +112,13 @@ def download_model(model):
os.makedirs(f"models/tokenizers/{name}", exist_ok=True)
files = ["config.json", "tokenizer.json", "tokenizer_config.json"]
if tokt == TOKENIZER_TYPE.SPM:
files.append("tokenizer.model")
if tokt == TOKENIZER_TYPE.UGM:
files.append("spiece.model")
for file in files:
save_path = f"models/tokenizers/{name}/{file}"
if os.path.isfile(save_path):
@ -132,7 +141,7 @@ for model in models:
name = model["name"]
tokt = model["tokt"]
if tokt == TOKENIZER_TYPE.SPM:
if tokt == TOKENIZER_TYPE.SPM or tokt == TOKENIZER_TYPE.UGM:
continue
# Skip if the tokenizer folder does not exist or there are other download issues previously
@ -142,7 +151,10 @@ for model in models:
# create the tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
if name == "t5":
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
else:
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
except OSError as e:
logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
continue # Skip to the next model if the tokenizer can't be loaded
@ -263,6 +275,7 @@ tests = [
"\n =",
"' era",
"Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
"!!!!!!",
"3",
"33",
"333",
@ -272,7 +285,8 @@ tests = [
"3333333",
"33333333",
"333333333",
# "Cửa Việt", # llama-bpe fails on this
"Cửa Việt", # llama-bpe fails on this
" discards",
chktxt,
]
@ -300,7 +314,10 @@ for model in models:
# create the tokenizer
try:
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
if name == "t5":
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
else:
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
except OSError as e:
logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
continue # Skip this model and continue with the next one in the loop

0
convert-llama-ggml-to-gguf.py → convert_llama_ggml_to_gguf.py
View File

34
examples/batched/batched.cpp
View File

@ -93,14 +93,34 @@ int main(int argc, char ** argv) {
// create a llama_batch
// we use this object to submit token data for decoding
llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t)n_parallel), 0, 1);
llama_batch batch = llama_batch_init(std::max(tokens_list.size(), (size_t) n_parallel), 0, n_parallel);
std::vector<llama_seq_id> seq_ids(n_parallel, 0);
for (int32_t i = 0; i < n_parallel; ++i) {
seq_ids[i] = i;
}
// evaluate the initial prompt
for (size_t i = 0; i < tokens_list.size(); ++i) {
llama_batch_add(batch, tokens_list[i], i, { 0 }, false);
llama_batch_add(batch, tokens_list[i], i, seq_ids, false);
}
GGML_ASSERT(batch.n_tokens == (int) tokens_list.size());
if (llama_model_has_encoder(model)) {
if (llama_encode(ctx, batch)) {
LOG_TEE("%s : failed to eval\n", __func__);
return 1;
}
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == -1) {
decoder_start_token_id = llama_token_bos(model);
}
llama_batch_clear(batch);
llama_batch_add(batch, decoder_start_token_id, 0, seq_ids, false);
}
// llama_decode will output logits only for the last token of the prompt
batch.logits[batch.n_tokens - 1] = true;
@ -109,11 +129,11 @@ int main(int argc, char ** argv) {
return 1;
}
// assign the system KV cache to all parallel sequences
// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
for (int32_t i = 1; i < n_parallel; ++i) {
llama_past_seq_cp(ctx, 0, i, -1, -1);
}
//// assign the system KV cache to all parallel sequences
//// this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
//for (int32_t i = 1; i < n_parallel; ++i) {
// llama_past_seq_cp(ctx, 0, i, -1, -1);
//}
if (n_parallel > 1) {
LOG_TEE("\n\n%s: generating %d sequences ...\n", __func__, n_parallel);

1
examples/embedding/README.md
View File

@ -58,4 +58,3 @@ The above command will output space-separated float values.
```powershell
embedding.exe -p 'Castle<#sep#>Stronghold<#sep#>Dog<#sep#>Cat' --embd-separator '<#sep#>' --embd-normalize 2 --embd-output-format '' -m './path/to/model.gguf' --n-gpu-layers 99 --log-disable 2>/dev/null
```

1
examples/infill/infill.cpp
View File

@ -659,4 +659,3 @@ int main(int argc, char ** argv) {
return 0;
}

2
examples/llava/requirements.txt
View File

@ -1,3 +1,3 @@
-r ../../requirements/requirements-convert-legacy-llama.txt
pillow~=10.2.0
torch~=2.1.1
torch~=2.2.1

1
examples/lookup/README.md
View File

@ -10,4 +10,3 @@ More info:
https://github.com/ggerganov/llama.cpp/pull/4484
https://github.com/ggerganov/llama.cpp/issues/4226

1
examples/main-cmake-pkg/.gitignore vendored
View File

@ -48,4 +48,3 @@
build*/
out/
tmp/

1
examples/main-cmake-pkg/CMakeLists.txt
View File

@ -30,4 +30,3 @@ target_include_directories(${TARGET} PRIVATE ${_common_path})
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

22
examples/main/main.cpp
View File

@ -255,7 +255,9 @@ int main(int argc, char ** argv) {
}
const bool add_bos = llama_should_add_bos_token(model);
GGML_ASSERT(llama_add_eos_token(model) != 1);
if (!llama_model_has_encoder(model)) {
GGML_ASSERT(llama_add_eos_token(model) != 1);
}
LOG("add_bos: %d\n", add_bos);
std::vector<llama_token> embd_inp;
@ -520,6 +522,24 @@ int main(int argc, char ** argv) {
exit(1);
}
if (llama_model_has_encoder(model)) {
int enc_input_size = embd_inp.size();
llama_token * enc_input_buf = embd_inp.data();
if (llama_encode(ctx, llama_batch_get_one(enc_input_buf, enc_input_size, 0, 0))) {
LOG_TEE("%s : failed to eval\n", __func__);
return 1;
}
llama_token decoder_start_token_id = llama_model_decoder_start_token(model);
if (decoder_start_token_id == -1) {
decoder_start_token_id = llama_token_bos(model);
}
embd_inp.clear();
embd_inp.push_back(decoder_start_token_id);
}
while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
// predict
if (!embd.empty()) {

9
examples/perplexity/perplexity.cpp
View File

@ -1991,6 +1991,12 @@ int main(int argc, char ** argv) {
params.n_batch = std::min(params.n_batch, n_kv);
} else {
params.n_batch = std::min(params.n_batch, params.n_ctx);
if (params.kl_divergence) {
params.n_parallel = 1;
} else {
// ensure there's at least enough seq_ids for HellaSwag
params.n_parallel = std::max(4, params.n_parallel);
}
}
if (params.ppl_stride > 0) {
@ -2015,9 +2021,6 @@ int main(int argc, char ** argv) {
llama_model * model;
llama_context * ctx;
// ensure there's at least enough seq_ids for HellaSwag
params.n_parallel = std::max(4, params.n_parallel);
// load the model and apply lora adapter, if any
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == NULL) {

1
examples/server-embd.py
View File

@ -31,4 +31,3 @@ for i in range(n-1):
embedding2 = np.array(result[j])
similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
print(f"Similarity between {i} and {j}: {similarity:.2f}")

1
examples/server/tests/features/passkey.feature
View File

@ -52,4 +52,3 @@ Feature: Passkey / Self-extend with context shift
#| TheBloke/Llama-2-7B-GGUF | llama-2-7b.Q2_K.gguf | 4096 | 3 | 16384 | 512 | 4 | 512 | 500 | 300 | 1234 | 5 | 1234 |
#| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768 | 2 | 16384 | 512 | 4 | 512 | 500 | 100 | 0987 | 5 | 0
# 987 |

1
examples/server/themes/buttons-top/index.html
View File

@ -1054,4 +1054,3 @@
</body>
</html>

1
examples/server/themes/wild/index.html
View File

@ -1058,4 +1058,3 @@
</body>
</html>

1
examples/sycl/run-llama2.sh
View File

@ -34,4 +34,3 @@ fi
#use multiple GPUs with same max compute units
#ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0

1
examples/sycl/win-build-sycl.bat
View File

@ -31,4 +31,3 @@ exit /B 0
:ERROR
echo comomand error: %errorlevel%
exit /B %errorlevel%

2
examples/sycl/win-run-llama2.bat
View File

@ -7,5 +7,3 @@ set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
.\build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0

8
examples/tokenize/tokenize.cpp
View File

@ -30,6 +30,7 @@ static void print_usage_information(const char * argv0, FILE * stream) {
fprintf(stream, " --stdin read prompt from standard input.\n");
fprintf(stream, " --no-bos do not ever add a BOS token to the prompt, even if normally the model uses a BOS token.\n");
fprintf(stream, " --log-disable disable logs. Makes stderr quiet when loading the model.\n");
fprintf(stream, " --show-count print the total number of tokens.\n");
}
static void llama_log_callback_null(ggml_log_level level, const char * text, void * user_data) {
@ -195,6 +196,7 @@ int main(int raw_argc, char ** raw_argv) {
bool printing_ids = false;
bool no_bos = false;
bool disable_logging = false;
bool show_token_count = false;
const char * model_path = NULL;
const char * prompt_path = NULL;
const char * prompt_arg = NULL;
@ -249,6 +251,9 @@ int main(int raw_argc, char ** raw_argv) {
else if (arg == "--log-disable") {
disable_logging = true;
}
else if (arg == "--show-count") {
show_token_count = true;
}
else {
fprintf(stderr, "Error: unknown option '%s'\n", argv[iarg].c_str());
return 1;
@ -384,6 +389,9 @@ int main(int raw_argc, char ** raw_argv) {
printf("]\n");
}
if (show_token_count) {
printf("Total number of tokens: %ld\n", tokens.size());
}
// silence valgrind
llama_free(ctx);
llama_free_model(model);

6
flake.lock
View File

@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1718895438,
"narHash": "sha256-k3JqJrkdoYwE3fHE6xGDY676AYmyh4U2Zw+0Bwe5DLU=",
"lastModified": 1719506693,
"narHash": "sha256-C8e9S7RzshSdHB7L+v9I51af1gDM5unhJ2xO1ywxNH8=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "d603719ec6e294f034936c0d0dc06f689d91b6c3",
"rev": "b2852eb9365c6de48ffb0dc2c9562591f652242a",
"type": "github"
},
"original": {

1
ggml/include/ggml-metal.h
View File

@ -63,4 +63,3 @@ GGML_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend);
#ifdef __cplusplus
}
#endif

8
ggml/src/CMakeLists.txt
View File

@ -486,9 +486,11 @@ if (GGML_SYCL)
add_compile_options(-I./) #include DPCT
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
if (GGML_SYCL_TARGET STREQUAL "NVIDIA")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
else()
add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
endif()
file(GLOB GGML_HEADERS_SYCL "ggml-sycl/*.hpp")
@ -1166,7 +1168,9 @@ target_link_libraries(ggml PRIVATE Threads::Threads ${GGML_EXTRA_LIBS})
find_library(MATH_LIBRARY m)
if (MATH_LIBRARY)
target_link_libraries(ggml PRIVATE ${MATH_LIBRARY})
if (NOT WIN32 OR NOT GGML_SYCL)
target_link_libraries(ggml PRIVATE ${MATH_LIBRARY})
endif()
endif()
if (BUILD_SHARED_LIBS)

14
ggml/src/ggml-common.h
View File

@ -106,19 +106,19 @@ typedef sycl::half2 ggml_half2;
#define QR6_K 2
#define QI2_XXS (QK_K / (4*QR2_XXS))
#define QR2_XXS 8
#define QR2_XXS 4
#define QI2_XS (QK_K / (4*QR2_XS))
#define QR2_XS 8
#define QR2_XS 4
#define QI2_S (QK_K / (4*QR2_S))
#define QR2_S 8
#define QR2_S 4
#define QI3_XXS (QK_K / (4*QR3_XXS))
#define QR3_XXS 8
#define QR3_XXS 4
#define QI3_XS (QK_K / (4*QR3_XS))
#define QR3_XS 8
#define QR3_XS 4
#define QI1_S (QK_K / (4*QR1_S))
#define QR1_S 8
@ -130,10 +130,10 @@ typedef sycl::half2 ggml_half2;
#define QR4_NL 2
#define QI4_XS (QK_K / (4*QR4_XS))
#define QR4_XS 8
#define QR4_XS 2
#define QI3_S (QK_K / (4*QR3_S))
#define QR3_S 8
#define QR3_S 4
#endif // GGML_COMMON_DECL_CUDA || GGML_COMMON_DECL_HIP

59
ggml/src/ggml-cuda.cu
View File

@ -1882,6 +1882,11 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
bool use_mul_mat_q = ggml_is_quantized(src0->type)
&& src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32;
// if mmvq is available it's a better choice than dmmv:
#ifndef GGML_CUDA_FORCE_DMMV
use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q;
#endif // GGML_CUDA_FORCE_DMMV
bool any_gpus_with_slow_fp16 = false;
if (split) {
@ -1894,22 +1899,15 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
}
const int cc = ggml_cuda_info().devices[id].cc;
use_mul_mat_vec_q = use_mul_mat_vec_q && cc >= MIN_CC_DP4A;
use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc);
}
} else {
const int cc = ggml_cuda_info().devices[ctx.device].cc;
use_mul_mat_vec_q = use_mul_mat_vec_q && cc >= MIN_CC_DP4A;
use_mul_mat_q = use_mul_mat_q && ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[1]);
any_gpus_with_slow_fp16 = any_gpus_with_slow_fp16 || !fast_fp16_available(cc);
}
// if mmvq is available it's a better choice than dmmv:
#ifndef GGML_CUDA_FORCE_DMMV
use_dequantize_mul_mat_vec = use_dequantize_mul_mat_vec && !use_mul_mat_vec_q;
#endif // GGML_CUDA_FORCE_DMMV
// debug helpers
//printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
//printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
@ -2713,27 +2711,40 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons
case GGML_OP_MUL_MAT:
case GGML_OP_MUL_MAT_ID:
{
struct ggml_tensor * a;
struct ggml_tensor * b;
struct ggml_tensor * a = op->src[0];
if (op->op == GGML_OP_MUL_MAT) {
a = op->src[0];
b = op->src[1];
} else {
a = op->src[2];
b = op->src[1];
}
if (a->ne[3] != b->ne[3]) {
return false;
}
ggml_type a_type = a->type;
if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS ||
a_type == GGML_TYPE_IQ1_S || a_type == GGML_TYPE_IQ4_NL || a_type == GGML_TYPE_IQ3_S ||
a_type == GGML_TYPE_IQ1_M || a_type == GGML_TYPE_IQ2_S || a_type == GGML_TYPE_IQ4_XS) {
if (b->ne[1] == 1 && ggml_nrows(b) > 1) {
struct ggml_tensor * b = op->src[1];
if (a->ne[3] != b->ne[3]) {
return false;
}
}
return true;
switch (a->type) {
case GGML_TYPE_F32:
case GGML_TYPE_F16:
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
case GGML_TYPE_Q8_K:
case GGML_TYPE_IQ1_M:
case GGML_TYPE_IQ1_S:
case GGML_TYPE_IQ2_S:
case GGML_TYPE_IQ2_XS:
case GGML_TYPE_IQ2_XXS:
case GGML_TYPE_IQ3_S:
case GGML_TYPE_IQ3_XXS:
case GGML_TYPE_IQ4_NL:
case GGML_TYPE_IQ4_XS:
return true;
default:
return false;
}
} break;
case GGML_OP_GET_ROWS:
{

80
ggml/src/ggml-cuda/common.cuh
View File

@ -3,6 +3,7 @@
#include "ggml.h"
#include "ggml-cuda.h"
#include <cstdint>
#include <memory>
#if defined(GGML_USE_HIPBLAS)
@ -226,6 +227,10 @@ typedef float2 dfloat2;
#define RDNA2
#endif
#if defined(__gfx1010__) || defined(__gfx1012__)
#define RDNA1
#endif
#ifndef __has_builtin
#define __has_builtin(x) 0
#endif
@ -268,30 +273,15 @@ static __device__ __forceinline__ unsigned int __vcmpeq4(unsigned int a, unsigne
return c;
}
static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
c = __builtin_amdgcn_sdot4(a, b, c, false);
#elif defined(RDNA3)
c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
#elif defined(__gfx1010__) || defined(__gfx900__)
int tmp1;
int tmp2;
asm("\n \
v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
v_add3_u32 %0, %1, %2, %0 \n \
v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
v_add3_u32 %0, %1, %2, %0 \n \
"
: "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
: "v"(a), "v"(b)
);
#else
const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
#endif
static __device__ __forceinline__ unsigned int __vcmpne4(unsigned int a, unsigned int b) {
const uint8x4_t& va = reinterpret_cast<const uint8x4_t&>(a);
const uint8x4_t& vb = reinterpret_cast<const uint8x4_t&>(b);
unsigned int c;
uint8x4_t& vc = reinterpret_cast<uint8x4_t&>(c);
#pragma unroll
for (int i = 0; i < 4; ++i) {
vc[i] = va[i] == vb[i] ? 0x00 : 0xff;
}
return c;
}
@ -467,8 +457,48 @@ static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half
}
#endif // CUDART_VERSION < 12000
static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, int c) {
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
c = __builtin_amdgcn_sdot4(a, b, c, false);
#elif defined(RDNA3)
c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
#elif defined(__gfx1010__) || defined(__gfx900__)
int tmp1;
int tmp2;
asm("\n \
v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
v_add3_u32 %0, %1, %2, %0 \n \
v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
v_add3_u32 %0, %1, %2, %0 \n \
"
: "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
: "v"(a), "v"(b)
);
#else
const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
#endif
return c;
#else // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
#if __CUDA_ARCH__ >= MIN_CC_DP4A
return __dp4a(a, b, c);
#else // __CUDA_ARCH__ >= MIN_CC_DP4A
const int8_t * a8 = (const int8_t *) &a;
const int8_t * b8 = (const int8_t *) &b;
return c + a8[0]*b8[0] + a8[1]*b8[1] + a8[2]*b8[2] + a8[3]*b8[3];
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
}
// TODO: move to ggml-common.h
static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
static constexpr __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113};
typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v);

1
ggml/src/ggml-cuda/cpy.cu
View File

@ -487,4 +487,3 @@ void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) {
GGML_ASSERT(false);
}
}

50
ggml/src/ggml-cuda/fattn-common.cuh
View File

@ -54,12 +54,11 @@ typedef float (*vec_dot_KQ_f32_t)(
template<typename T, int D>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A
const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c;
GGML_UNUSED(Q_v);
half sum = 0.0f;
T sum = 0.0f;
#pragma unroll
for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) {
@ -72,7 +71,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
const int v = (get_int_from_uint8(K_q4_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
const int u = Q_q8[k_KQ_0/WARP_SIZE];
const int sumi = __dp4a(v, u, 0);
const int sumi = ggml_cuda_dp4a(v, u, 0);
#ifdef FP16_AVAILABLE
if (std::is_same<T, half>::value) {
@ -90,19 +89,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0(
}
return sum;
#else
GGML_UNUSED(K_c);
GGML_UNUSED(Q_v);
GGML_UNUSED(Q_q8);
GGML_UNUSED(Q_ds_v);
NO_DEVICE_CODE;
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
template<typename T, int D>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A
const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c;
GGML_UNUSED(Q_v);
@ -120,7 +111,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
const int v = (get_int_from_uint8_aligned(K_q4_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F;
const int u = Q_q8[k_KQ_0/WARP_SIZE];
const int sumi = __dp4a(v, u, 0);
const int sumi = ggml_cuda_dp4a(v, u, 0);
#ifdef FP16_AVAILABLE
if (std::is_same<T, half>::value) {
@ -142,19 +133,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1(
}
return sum;
#else
GGML_UNUSED(K_c);
GGML_UNUSED(Q_v);
GGML_UNUSED(Q_q8);
GGML_UNUSED(Q_ds_v);
NO_DEVICE_CODE;
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
template<typename T, int D>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A
const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c;
GGML_UNUSED(Q_v);
@ -179,7 +162,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
const int u = Q_q8[k_KQ_0/WARP_SIZE];
const int sumi = __dp4a(v, u, 0);
const int sumi = ggml_cuda_dp4a(v, u, 0);
#ifdef FP16_AVAILABLE
if (std::is_same<T, half>::value) {
@ -197,19 +180,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0(
}
return sum;
#else
GGML_UNUSED(K_c);
GGML_UNUSED(Q_v);
GGML_UNUSED(Q_q8);
GGML_UNUSED(Q_ds_v);
NO_DEVICE_CODE;
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
template<typename T, int D>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A
const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c;
GGML_UNUSED(Q_v);
@ -234,7 +209,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
const int u = Q_q8[k_KQ_0/WARP_SIZE];
const int sumi = __dp4a(v, u, 0);
const int sumi = ggml_cuda_dp4a(v, u, 0);
#ifdef FP16_AVAILABLE
if (std::is_same<T, half>::value) {
@ -256,19 +231,11 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1(
}
return sum;
#else
GGML_UNUSED(K_c);
GGML_UNUSED(Q_v);
GGML_UNUSED(Q_q8);
GGML_UNUSED(Q_ds_v);
NO_DEVICE_CODE;
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
template <typename T, int D>
static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0(
const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) {
#if __CUDA_ARCH__ >= MIN_CC_DP4A
const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c;
GGML_UNUSED(Q_v);
@ -297,13 +264,6 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0(
}
return sum;
#else
GGML_UNUSED(K_c);
GGML_UNUSED(Q_v);
GGML_UNUSED(Q_q8);
GGML_UNUSED(Q_ds_v);
NO_DEVICE_CODE;
#endif // __CUDA_ARCH__ >= MIN_CC_DP4A
}
template <typename T, int D>

10
ggml/src/ggml-cuda/mmq.cuh
View File

@ -60,12 +60,16 @@ static constexpr __device__ int get_mmq_x_max_device() {
}
static constexpr int get_mmq_y_host(const int cc) {
return int8_mma_available(cc) || cc >= CC_VOLTA ? 128 : 64;
return cc >= CC_OFFSET_AMD ? (cc == CC_RDNA1 ? 64 : 128) : (cc >= CC_VOLTA ? 128 : 64);
}
static constexpr __device__ int get_mmq_y_device() {
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
#if defined(RDNA1)
return 64;
#else
return 128;
#endif // defined RDNA1
#else
#if __CUDA_ARCH__ >= CC_VOLTA
return 128;
@ -2259,9 +2263,9 @@ static __device__ void mul_mat_q_process_tile(
template <ggml_type type, int mmq_x, int nwarps, bool need_check>
#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)
#if defined(RDNA3) || defined(RDNA2)
#if defined(RDNA3) || defined(RDNA2) || defined(RDNA1)
__launch_bounds__(WARP_SIZE*nwarps, 2)
#endif // defined(RDNA3) || defined(RDNA2)
#endif // defined(RDNA3) || defined(RDNA2) || defined(RDNA1)
#else
#if __CUDA_ARCH__ >= CC_VOLTA
__launch_bounds__(WARP_SIZE*nwarps, 1)

26
ggml/src/ggml-cuda/mmvq.cu
View File

@ -28,16 +28,22 @@ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type)
static constexpr __device__ int get_vdr_mmvq(ggml_type type) {
return type == GGML_TYPE_Q4_0 ? VDR_Q4_0_Q8_1_MMVQ :
type == GGML_TYPE_Q4_1 ? VDR_Q4_1_Q8_1_MMVQ :
type == GGML_TYPE_Q5_0 ? VDR_Q5_0_Q8_1_MMVQ :
type == GGML_TYPE_Q5_1 ? VDR_Q5_1_Q8_1_MMVQ :
type == GGML_TYPE_Q8_0 ? VDR_Q8_0_Q8_1_MMVQ :
type == GGML_TYPE_Q2_K ? VDR_Q2_K_Q8_1_MMVQ :
type == GGML_TYPE_Q3_K ? VDR_Q3_K_Q8_1_MMVQ :
type == GGML_TYPE_Q4_K ? VDR_Q4_K_Q8_1_MMVQ :
type == GGML_TYPE_Q5_K ? VDR_Q5_K_Q8_1_MMVQ :
type == GGML_TYPE_Q6_K ? VDR_Q6_K_Q8_1_MMVQ :
type == GGML_TYPE_IQ4_NL ? VDR_Q4_K_Q8_1_MMVQ :
type == GGML_TYPE_Q4_1 ? VDR_Q4_1_Q8_1_MMVQ :
type == GGML_TYPE_Q5_0 ? VDR_Q5_0_Q8_1_MMVQ :
type == GGML_TYPE_Q5_1 ? VDR_Q5_1_Q8_1_MMVQ :
type == GGML_TYPE_Q8_0 ? VDR_Q8_0_Q8_1_MMVQ :
type == GGML_TYPE_Q2_K ? VDR_Q2_K_Q8_1_MMVQ :
type == GGML_TYPE_Q3_K ? VDR_Q3_K_Q8_1_MMVQ :
type == GGML_TYPE_Q4_K ? VDR_Q4_K_Q8_1_MMVQ :
type == GGML_TYPE_Q5_K ? VDR_Q5_K_Q8_1_MMVQ :
type == GGML_TYPE_Q6_K ? VDR_Q6_K_Q8_1_MMVQ :
type == GGML_TYPE_IQ2_XXS ? VDR_IQ2_XXS_Q8_1_MMVQ :
type == GGML_TYPE_IQ2_XS ? VDR_IQ2_XS_Q8_1_MMVQ :
type == GGML_TYPE_IQ2_S ? VDR_IQ2_S_Q8_1_MMVQ :
type == GGML_TYPE_IQ3_XXS ? VDR_IQ3_XXS_Q8_1_MMVQ :
type == GGML_TYPE_IQ3_S ? VDR_IQ3_S_Q8_1_MMVQ :
type == GGML_TYPE_IQ4_NL ? VDR_IQ4_NL_Q8_1_MMVQ :
type == GGML_TYPE_IQ4_XS ? VDR_IQ4_XS_Q8_1_MMVQ :
1;
}

682
ggml/src/ggml-cuda/vecdotq.cuh
View File

File diff suppressed because it is too large Load Diff

1
ggml/src/ggml-metal.metal
View File

@ -6537,4 +6537,3 @@ template [[host_name("kernel_mul_mv_id_iq3_s_f32")]] kernel kernel_mul_mv_id_t
template [[host_name("kernel_mul_mv_id_iq2_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_s_f32_impl>>;
template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_nl_f32_impl>>;
template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_xs_f32_impl>>;

1
ggml/src/ggml-quants.h
View File

@ -130,4 +130,3 @@ void iq3xs_free_impl(int grid_size);
#ifdef __cplusplus
}
#endif

777
ggml/src/ggml-sycl.cpp
View File

@ -74,51 +74,6 @@ typedef void (*ggml_sycl_op_flatten_t)(ggml_backend_sycl_context & ctx, const gg
const float *src1_dd, float *dst_dd,
const queue_ptr &main_stream);
static __dpct_inline__ float warp_reduce_sum(float x,
const sycl::nd_item<3> &item_ct1) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
/*
DPCT1096:98: The right-most dimension of the work-group used in the SYCL
kernel that calls this function may be less than "32". The function
"dpct::permute_sub_group_by_xor" may return an unexpected result on the
CPU device. Modify the size of the work-group to ensure that the value
of the right-most dimension is a multiple of "32".
*/
x += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), x, mask);
}
return x;
}
static __dpct_inline__ sycl::float2
warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3> &item_ct1) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
a.x() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.x(),
mask);
a.y() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.y(),
mask);
}
return a;
}
static __dpct_inline__ float warp_reduce_max(float x,
const sycl::nd_item<3> &item_ct1) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
/*
DPCT1096:97: The right-most dimension of the work-group used in the SYCL
kernel that calls this function may be less than "32". The function
"dpct::permute_sub_group_by_xor" may return an unexpected result on the
CPU device. Modify the size of the work-group to ensure that the value
of the right-most dimension is a multiple of "32".
*/
x = sycl::fmax(x, dpct::permute_sub_group_by_xor(
item_ct1.get_sub_group(), x, mask));
}
return x;
}
static __dpct_inline__ float op_repeat(const float a, const float b) {
return b;
GGML_UNUSED(a);
@ -336,47 +291,6 @@ static void sqr_f32(const float * x, float * dst, const int k,
dst[i] = x[i] * x[i];
}
static void norm_f32(const float * x, float * dst, const int ncols, const float eps,
const sycl::nd_item<3> &item_ct1, sycl::float2 *s_sum, int block_size) {
const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
item_ct1.get_local_id(1);
const int tid = item_ct1.get_local_id(2);
sycl::float2 mean_var = sycl::float2(0.f, 0.f);
for (int col = tid; col < ncols; col += block_size) {
const float xi = x[row*ncols + col];
mean_var.x() += xi;
mean_var.y() += xi * xi;
}
// sum up partial sums
mean_var = warp_reduce_sum(mean_var, item_ct1);
if (block_size > WARP_SIZE) {
int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
if (lane_id == 0) {
s_sum[warp_id] = mean_var;
}
/*
DPCT1118:0: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
item_ct1.barrier(sycl::access::fence_space::local_space);
mean_var = s_sum[lane_id];
mean_var = warp_reduce_sum(mean_var, item_ct1);
}
const float mean = mean_var.x() / ncols;
const float var = mean_var.y() / ncols - mean * mean;
const float inv_std = sycl::rsqrt(var + eps);
for (int col = tid; col < ncols; col += block_size) {
dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std;
}
}
static void concat_f32(const float *x,const float *y, float *dst, const int ne0, const int ne02,
const sycl::nd_item<3> &item_ct1) {
int nidx = item_ct1.get_local_id(2) +
@ -444,126 +358,11 @@ static void pad_f32(const float *x, float *dst, const int ne0, const int ne00,
}
}
static void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps,
const sycl::nd_item<3> &item_ct1, float *s_sum, int block_size) {
int start = item_ct1.get_group(2) * group_size;
int end = start + group_size;
start += item_ct1.get_local_id(2);
if (end >= ne_elements) {
end = ne_elements;
}
float tmp = 0.0f; // partial sum for thread in warp
for (int j = start; j < end; j += block_size) {
tmp += x[j];
}
tmp = warp_reduce_sum(tmp, item_ct1);
if (block_size > WARP_SIZE) {
int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
if (lane_id == 0) {
s_sum[warp_id] = tmp;
}
/*
DPCT1118:1: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
/*
DPCT1065:54: Consider replacing sycl::nd_item::barrier() with
sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
better performance if there is no access to global memory.
*/
item_ct1.barrier();
tmp = s_sum[lane_id];
tmp = warp_reduce_sum(tmp, item_ct1);
}
float mean = tmp / group_size;
tmp = 0.0f;
for (int j = start; j < end; j += block_size) {
float xi = x[j] - mean;
dst[j] = xi;
tmp += xi * xi;
}
tmp = warp_reduce_sum(tmp, item_ct1);
if (block_size > WARP_SIZE) {
int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
if (lane_id == 0) {
s_sum[warp_id] = tmp;
}
/*
DPCT1118:2: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
/*
DPCT1065:55: Consider replacing sycl::nd_item::barrier() with
sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
better performance if there is no access to global memory.
*/
item_ct1.barrier();
tmp = s_sum[lane_id];
tmp = warp_reduce_sum(tmp, item_ct1);
}
float variance = tmp / group_size;
float scale = sycl::rsqrt(variance + eps);
for (int j = start; j < end; j += block_size) {
dst[j] *= scale;
}
}
static void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps,
const sycl::nd_item<3> &item_ct1, float *s_sum, int block_size) {
const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
item_ct1.get_local_id(1);
const int tid = item_ct1.get_local_id(2);
float tmp = 0.0f; // partial sum for thread in warp
for (int col = tid; col < ncols; col += block_size) {
const float xi = x[row*ncols + col];
tmp += xi * xi;
}
// sum up partial sums
tmp = warp_reduce_sum(tmp, item_ct1);
if (block_size > WARP_SIZE) {
int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
if (lane_id == 0) {
s_sum[warp_id] = tmp;
}
/*
DPCT1118:3: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
item_ct1.barrier(sycl::access::fence_space::local_space);
tmp = s_sum[lane_id];
tmp = warp_reduce_sum(tmp, item_ct1);
}
const float mean = tmp / ncols;
const float scale = sycl::rsqrt(mean + eps);
for (int col = tid; col < ncols; col += block_size) {
dst[row*ncols + col] = scale * x[row*ncols + col];
}
}
template<int QUANT_BLOCK_TILE>
static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded,
const sycl::nd_item<3> &item_ct1) {
const int ix = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
item_ct1.get_local_id(2);
const int ix = (item_ct1.get_local_range(2) * item_ct1.get_group(2) +
item_ct1.get_local_id(2)) * QUANT_BLOCK_TILE;
if (ix >= kx_padded) {
return;
@ -578,23 +377,39 @@ static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy,
const int ib = i_padded / QK8_1; // block index
const int iqs = i_padded % QK8_1; // quant index
const float xi = ix < kx ? x[iy*kx + ix] : 0.0f;
float amax = sycl::fabs((float)xi);
float sum = xi;
typedef sycl::vec<float, QUANT_BLOCK_TILE> TC;
typedef sycl::vec<int8_t, QUANT_BLOCK_TILE> TQ;
TC zeros;
TQ qzeros;
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
amax = sycl::fmax(amax, dpct::permute_sub_group_by_xor(
item_ct1.get_sub_group(), amax, mask));
sum +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), sum, mask);
for (int i = 0; i < QUANT_BLOCK_TILE; i++)
{
zeros[i] = 0.f;
qzeros[i] = 0;
}
const TC xi = ix < kx ? *(TC *)&x[iy * kx + ix] : zeros;
float sum = xi[0];
float amax = sycl::fabs(xi[0]);
#pragma unroll
for (int i = 1; i < QUANT_BLOCK_TILE; i++)
{
sum += xi[i];
amax = sycl::fmax(sycl::fabs(xi[i]), amax);
}
sum = warp_reduce_sum(sum, item_ct1);
amax = warp_reduce_max(amax, item_ct1);
const float d = amax / 127;
const int8_t q = amax == 0.0f ? 0 : sycl::round(xi / d);
TQ q = qzeros;
if (amax != 0.0f)
{
#pragma unroll
for (int i = 0; i < QUANT_BLOCK_TILE; i++) {
q[i] = sycl::round(xi[i] / d);
}
}
y[ib].qs[iqs] = q;
*(TQ *)&y[ib].qs[iqs] = q;
if (iqs > 0) {
return;
@ -728,7 +543,7 @@ static void mul_mat_p021_f16_f32(
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -781,7 +596,7 @@ static void mul_mat_vec_nc_f16_f32( // nc == non-contiguous
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -978,114 +793,6 @@ static void cpy_f32_q(const char * cx, char * cdst, const int ne,
cpy_blck(cx + x_offset, cdst + dst_offset);
}
static float rope_yarn_ramp(const float low, const float high, const int i0) {
const float y = (i0 / 2 - low) / sycl::max(0.001f, high - low);
return 1.0f - sycl::min(1.0f, sycl::max(0.0f, y));
}
struct rope_corr_dims {
float v[4];
};
// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
static void rope_yarn(
float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
float * cos_theta, float * sin_theta
) {
// Get n-d rotational scaling corrected for extrapolation
float theta_interp = freq_scale * theta_extrap;
float theta = theta_interp;
if (ext_factor != 0.0f) {
float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
// Get n-d magnitude scaling corrected for interpolation
mscale *= 1.0f + 0.1f * sycl::log(1.0f / freq_scale);
}
*cos_theta = sycl::cos(theta) * mscale;
*sin_theta = sycl::sin(theta) * mscale;
}
// rope == RoPE == rotary positional embedding
template<typename T, bool has_pos>
static void rope(
const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base,
float ext_factor, float attn_factor, rope_corr_dims corr_dims
,
const sycl::nd_item<3> &item_ct1) {
const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
item_ct1.get_local_id(1));
if (col >= ncols) {
return;
}
const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
item_ct1.get_local_id(2);
const int i = row*ncols + col;
const int i2 = row/p_delta_rows;
const int p = has_pos ? pos[i2] : 0;
const float theta_base = p * dpct::pow(freq_base, -float(col) / ncols);
float cos_theta, sin_theta;
rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta);
const float x0 = x[i + 0];
const float x1 = x[i + 1];
dst[i + 0] = x0*cos_theta - x1*sin_theta;
dst[i + 1] = x0*sin_theta + x1*cos_theta;
}
template<typename T, bool has_pos, bool has_freq_facs>
static void rope_neox(
const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims,
const float * freq_factors, const sycl::nd_item<3> &item_ct1) {
const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
item_ct1.get_local_id(1));
if (col >= ncols) {
return;
}
const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
item_ct1.get_local_id(2);
const int ib = col / n_dims;
const int ic = col % n_dims;
if (ib > 0) {
const int i = row*ncols + ib*n_dims + ic;
dst[i + 0] = x[i + 0];
dst[i + 1] = x[i + 1];
return;
}
const int i = row*ncols + ib*n_dims + ic/2;
const int i2 = row/p_delta_rows;
float cur_rot = inv_ndims * ic - ib;
const int p = has_pos ? pos[i2] : 0;
const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f;
const float theta_base =
p * freq_scale * dpct::pow(theta_scale, col / 2.0f)/freq_factor;
float cos_theta, sin_theta;
rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta);
const float x0 = x[i + 0];
const float x1 = x[i + n_dims/2];
dst[i + 0] = x0*cos_theta - x1*sin_theta;
dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
}
static void k_sum_rows_f32(const float * x, float * dst, const int ncols,
const sycl::nd_item<3> &item_ct1) {
const int row = item_ct1.get_group(1);
@ -1751,99 +1458,6 @@ static void sqr_f32_sycl(const float *x, float *dst, const int k,
});
}
static void norm_f32_sycl(const float *x, float *dst, const int ncols,
const int nrows, const float eps,
queue_ptr stream) {
GGML_ASSERT(ncols % WARP_SIZE == 0);
if (ncols < 1024) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
stream->submit([&](sycl::handler &cgh) {
sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
sycl::range<1>(32), cgh);
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
norm_f32(x, dst, ncols, eps, item_ct1,
s_sum_acc_ct1.get_pointer(), WARP_SIZE);
});
});
} else {
const int work_group_size = get_work_group_size(stream->get_device());
const sycl::range<3> block_dims(1, 1, work_group_size);
/*
DPCT1049:17: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->submit([&](sycl::handler &cgh) {
sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
sycl::range<1>(32), cgh);
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
norm_f32(x, dst, ncols, eps, item_ct1,
s_sum_acc_ct1.get_pointer(), work_group_size);
});
});
}
}
static void group_norm_f32_sycl(const float *x, float *dst,
const int num_groups, const int group_size,
const int ne_elements, queue_ptr stream) {
static const float eps = 1e-6f;
if (group_size < 1024) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
stream->submit([&](sycl::handler &cgh) {
sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
cgh);
const float eps_ct4 = eps;
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
group_norm_f32(
x, dst, group_size, ne_elements, eps_ct4, item_ct1,
s_sum_acc_ct1.get_pointer(), WARP_SIZE);
});
});
} else {
const int work_group_size = get_work_group_size(stream->get_device());
const sycl::range<3> block_dims(1, 1, work_group_size);
/*
DPCT1049:18: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->submit([&](sycl::handler &cgh) {
sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
cgh);
const float eps_ct4 = eps;
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
group_norm_f32(x, dst, group_size, ne_elements,
eps_ct4, item_ct1,
s_sum_acc_ct1.get_pointer(), work_group_size);
});
});
}
}
static void concat_f32_sycl(const float *x, const float *y, float *dst,
const int ne0, int ne1, int ne2, int ne02,
queue_ptr stream) {
@ -1885,64 +1499,22 @@ static void pad_f32_sycl(const float *x, float *dst, const int ne00,
});
}
static void rms_norm_f32_sycl(const float *x, float *dst, const int ncols,
const int nrows, const float eps,
queue_ptr stream) {
GGML_ASSERT(ncols % WARP_SIZE == 0);
// printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
if (ncols < 1024) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
stream->submit([&](sycl::handler &cgh) {
sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
cgh);
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
rms_norm_f32(x, dst, ncols, eps, item_ct1,
s_sum_acc_ct1.get_pointer(), WARP_SIZE);
});
});
} else {
const int work_group_size = get_work_group_size(stream->get_device());
const sycl::range<3> block_dims(1, 1, work_group_size);
/*
DPCT1049:19: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->submit([&](sycl::handler &cgh) {
sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(32),
cgh);
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
rms_norm_f32(x, dst, ncols, eps, item_ct1,
s_sum_acc_ct1.get_pointer(), work_group_size);
});
});
}
}
static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx,
const int ky, const int kx_padded,
queue_ptr stream) {
const int block_num_x = (kx_padded + SYCL_QUANTIZE_BLOCK_SIZE - 1) / SYCL_QUANTIZE_BLOCK_SIZE;
const sycl::range<3> num_blocks(1, ky, block_num_x);
const sycl::range<3> block_size(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE);
int constexpr QUANT_BLOCK_TILE = QK8_1 / WARP_SIZE;
static_assert(QK8_1 % WARP_SIZE == 0);
const sycl::range<3> block_size(1, 1, SYCL_QUANTIZE_BLOCK_SIZE / QUANT_BLOCK_TILE);
{
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(num_blocks * block_size, block_size),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
quantize_q8_1(x, vy, kx, kx_padded, item_ct1);
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
quantize_q8_1<QUANT_BLOCK_TILE>(x, vy, kx, kx_padded, item_ct1);
});
}
}
@ -1962,7 +1534,7 @@ static void ggml_mul_mat_p021_f16_f32_sycl(const void *vx, const float *y,
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_p021_f16_f32(vx, y, dst, ncols_x, nrows_x, nchannels_x,
nchannels_y, item_ct1);
});
@ -1982,7 +1554,7 @@ static void ggml_mul_mat_vec_nc_f16_f32_sycl(
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_nc_f16_f32(vx, y, dst, ncols_x, nrows_x,
row_stride_x, channel_stride_x,
nchannels_y / nchannels_x, item_ct1);
@ -2241,117 +1813,13 @@ static void clamp_f32_sycl(const float *x, float *dst, const float min,
});
}
template <typename T>
static void rope_sycl(const T *x, T *dst, int ncols, int nrows,
const int32_t *pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor,
rope_corr_dims corr_dims, queue_ptr stream) {
GGML_ASSERT(ncols % 2 == 0);
const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
const sycl::range<3> block_nums(1, num_blocks_x, nrows);
if (pos == nullptr) {
/*
DPCT1049:40: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope<T, false>(x, dst, ncols, pos, freq_scale, p_delta_rows,
freq_base, ext_factor, attn_factor, corr_dims,
item_ct1);
});
} else {
/*
DPCT1049:41: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope<T, true>(x, dst, ncols, pos, freq_scale, p_delta_rows,
freq_base, ext_factor, attn_factor, corr_dims,
item_ct1);
});
}
}
template <typename T>
static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows,
const int32_t *pos, float freq_scale,
int p_delta_rows, float freq_base, float ext_factor,
float attn_factor, rope_corr_dims corr_dims,
const float * freq_factors, queue_ptr stream) {
GGML_ASSERT(ncols % 2 == 0);
const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
const sycl::range<3> block_nums(1, num_blocks_x, nrows);
const float theta_scale = powf(freq_base, -2.0f/n_dims);
const float inv_ndims = -1.0f / n_dims;
if (pos == nullptr) {
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
if (freq_factors == nullptr) {
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, false, false>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, freq_factors,
item_ct1);
});
} else {
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, false, true>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, freq_factors,
item_ct1);
});
}
} else {
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
if (freq_factors == nullptr) {
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, true, false>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1);
});
} else {
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, true, true>(x, dst, ncols, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1);
});
}
}
}
static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols,
const int nrows, queue_ptr stream) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
const sycl::range<3> block_nums(1, nrows, 1);
stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
k_sum_rows_f32(x, dst, ncols, item_ct1);
});
}
@ -2432,7 +1900,7 @@ static void soft_max_f32_submitter(const float * x, const float * mask, float *
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, dst, ncols_par,
nrows_y, scale, max_bias, m0,
m1, n_head_log2, item_ct1,
@ -2612,12 +2080,6 @@ static inline int get_sycl_env(const char *env_name, int default_val) {
return user_number;
}
static inline int get_work_group_size(const sycl::device& device) {
dpct::device_info prop;
dpct::get_device_info(prop, device);
return prop.get_max_work_group_size();
}
static void ggml_check_sycl() try {
static bool initialized = false;
@ -3176,45 +2638,6 @@ inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor
(void) src1_dd;
}
inline void ggml_sycl_op_norm(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst, const float *src0_dd,
const float *src1_dd, float *dst_dd,
const queue_ptr &main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
const int64_t ne00 = src0->ne[0];
const int64_t nrows = ggml_nrows(src0);
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
(void) src1;
(void) dst;
(void) src1_dd;
}
inline void ggml_sycl_op_group_norm(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1, ggml_tensor *dst,
const float *src0_dd, const float *src1_dd,
float *dst_dd,
const queue_ptr &main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
int num_groups = dst->op_params[0];
int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
group_norm_f32_sycl(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream);
(void) src1;
(void) dst;
(void) src1_dd;
}
inline void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1, ggml_tensor *dst,
const float *src0_dd, const float *src1_dd,
@ -3278,28 +2701,6 @@ inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor
(void) src1_dd;
}
inline void ggml_sycl_op_rms_norm(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1, ggml_tensor *dst,
const float *src0_dd, const float *src1_dd,
float *dst_dd,
const queue_ptr &main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
const int64_t ne00 = src0->ne[0];
const int64_t nrows = ggml_nrows(src0);
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
rms_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
(void) src1;
(void) dst;
(void) src1_dd;
}
static int64_t get_row_rounding(ggml_type type, const std::array<float, GGML_SYCL_MAX_DEVICES> & tensor_split) {
int64_t min_compute_capability = INT_MAX;
int64_t max_compute_capability = INT_MIN;
@ -3461,97 +2862,6 @@ catch (sycl::exception const &exc) {
std::exit(1);
}
inline void ggml_sycl_op_rope(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1,
ggml_tensor *dst, const float *src0_dd,
const float *src1_dd, float *dst_dd,
const queue_ptr &main_stream) {
const ggml_tensor * src2 = dst->src[2];
GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
GGML_ASSERT(src0->type == dst->type);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne2 = dst->ne[2];
const int64_t nrows = ggml_nrows(src0);
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
//const int n_ctx = ((int32_t *) dst->op_params)[3];
const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
// RoPE alteration for extended context
float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float));
memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
const float * freq_factors = nullptr;
const int32_t * pos = nullptr;
if ((mode & 1) == 0) {
GGML_ASSERT(src1->type == GGML_TYPE_I32);
GGML_ASSERT(src1->ne[0] == ne2);
pos = (const int32_t *) src1_dd;
}
const bool is_neox = mode & 2;
#pragma message("TODO: update rope NORM mode to match NEOX mode")
#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7634")
if (is_neox) {
pos = (const int32_t *) src1_dd;
if (src2 != nullptr) {
freq_factors = (const float *) src2->data;
}
} else {
GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox");
}
rope_corr_dims corr_dims;
ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims.v);
// compute
if (is_neox) {
if (src0->type == GGML_TYPE_F32) {
rope_neox_sycl(
(const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, freq_factors, main_stream
);
} else if (src0->type == GGML_TYPE_F16) {
rope_neox_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd,
ne00, n_dims, nrows, pos, freq_scale, ne01,
freq_base, ext_factor, attn_factor, corr_dims,
freq_factors, main_stream);
} else {
GGML_ASSERT(false);
}
} else {
if (src0->type == GGML_TYPE_F32) {
rope_sycl(
(const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, main_stream
);
} else if (src0->type == GGML_TYPE_F16) {
rope_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00,
nrows, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, main_stream);
} else {
GGML_ASSERT(false);
}
}
(void) src1;
(void) dst;
(void) src1_dd;
}
static void ggml_sycl_op_pool2d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1, ggml_tensor *dst,
const float *src0_dd, const float *src1_dd,
@ -4576,7 +3886,6 @@ bool ggml_sycl_supports_dmmv(enum ggml_type type) {
static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const bool split = ggml_backend_buffer_is_sycl_split(src0->buffer);
int64_t min_compute_capability = INT_MAX;
if (split) {
@ -6241,7 +5550,9 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons
case GGML_OP_CONT:
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_SOFT_MAX:
return true;
case GGML_OP_ROPE:
return ggml_is_contiguous(op->src[0]);
case GGML_OP_IM2COL:
case GGML_OP_POOL_2D:
case GGML_OP_SUM_ROWS:

2
ggml/src/ggml-sycl/backend.hpp
View File

@ -19,5 +19,7 @@
#include "dmmv.hpp"
#include "mmq.hpp"
#include "mmvq.hpp"
#include "rope.hpp"
#include "norm.hpp"
#endif // GGML_SYCL_BACKEND_HPP

61
ggml/src/ggml-sycl/common.hpp
View File

@ -295,5 +295,66 @@ struct ggml_backend_sycl_context {
}
};
// common host functions
static inline int get_work_group_size(const sycl::device& device) {
dpct::device_info prop;
dpct::get_device_info(prop, device);
return prop.get_max_work_group_size();
}
// common device functions
static __dpct_inline__ float warp_reduce_sum(float x,
const sycl::nd_item<3>& item_ct1) {
#pragma unroll
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
/*
DPCT1096:98: The right-most dimension of the work-group used in the SYCL
kernel that calls this function may be less than "32". The function
"dpct::permute_sub_group_by_xor" may return an unexpected result on the
CPU device. Modify the size of the work-group to ensure that the value
of the right-most dimension is a multiple of "32".
*/
x += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), x, mask);
}
return x;
}
static __dpct_inline__ sycl::float2
warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3>& item_ct1) {
#pragma unroll
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
a.x() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.x(),
mask);
a.y() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.y(),
mask);
}
return a;
}
static __dpct_inline__ float warp_reduce_max(float x,
const sycl::nd_item<3>& item_ct1) {
#pragma unroll
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
/*
DPCT1096:97: The right-most dimension of the work-group used in the SYCL
kernel that calls this function may be less than "32". The function
"dpct::permute_sub_group_by_xor" may return an unexpected result on the
CPU device. Modify the size of the work-group to ensure that the value
of the right-most dimension is a multiple of "32".
*/
x = sycl::fmax(x, dpct::permute_sub_group_by_xor(
item_ct1.get_sub_group(), x, mask));
}
return x;
}
// Helper for vec loading aligned data
template <typename Tp, int n>
inline sycl::vec<Tp, n> vec_aligned_load(const Tp* aligned_ptr) {
return *reinterpret_cast<const sycl::vec<Tp, n>*>(aligned_ptr);
}
#endif // GGML_SYCL_COMMON_HPP

7
ggml/src/ggml-sycl/convert.cpp
View File

@ -152,12 +152,15 @@ static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k,
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
stream->submit([&](sycl::handler &cgh) {
sycl::local_accessor<uint8_t, 1> scale_local_acc(sycl::range<1>(12), cgh);
cgh.parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) *
sycl::range<3>(1, 1, 32),
sycl::range<3>(1, 1, 32)),
[=](sycl::nd_item<3> item_ct1) {
dequantize_block_q4_K(vx, y, item_ct1);
dequantize_block_q4_K(vx, y, scale_local_acc.get_pointer(), item_ct1);
});
});
}
}

30
ggml/src/ggml-sycl/dequantize.hpp
View File

@ -293,7 +293,8 @@ static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restri
#if QK_K == 256
static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
if (j < 4) {
d = q[j] & 63; m = q[j + 4] & 63;
d = q[j] & 63;
m = q[j + 4] & 63;
} else {
d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4);
m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4);
@ -303,7 +304,7 @@ static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8
template<typename dst_t>
static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy,
const sycl::nd_item<3> &item_ct1) {
uint8_t* scales_local, const sycl::nd_item<3> &item_ct1) {
const block_q4_K * x = (const block_q4_K *) vx;
const int i = item_ct1.get_group(2);
@ -318,19 +319,26 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri
dst_t * y = yy + i*QK_K + 64*il + n*ir;
const float dall = x[i].dm[0];
const float dmin = x[i].dm[1];
const sycl::half2 dm = x[i].dm;
const float dall = dm[0];
const float dmin = dm[1];
const uint8_t * q = x[i].qs + 32*il + n*ir;
if (tid < 12)
scales_local[tid] = x[i].scales[tid];
item_ct1.barrier(sycl::access::fence_space::local_space);
uint8_t sc, m;
get_scale_min_k4(is + 0, x[i].scales, sc, m);
const float d1 = dall * sc; const float m1 = dmin * m;
get_scale_min_k4(is + 1, x[i].scales, sc, m);
const float d2 = dall * sc; const float m2 = dmin * m;
get_scale_min_k4(is + 0, scales_local, sc, m);
const float d1 = dall * sc;
const float m1 = dmin * m;
get_scale_min_k4(is + 1, scales_local, sc, m);
const float d2 = dall * sc;
const float m2 = dmin * m;
sycl::vec<uint8_t, n> q_vec = vec_aligned_load<uint8_t, n>(x[i].qs + 32*il + n*ir);
for (int l = 0; l < n; ++l) {
y[l + 0] = d1 * (q[l] & 0xF) - m1;
y[l +32] = d2 * (q[l] >> 4) - m2;
y[l + 0] = d1 * (q_vec[l] & 0xF) - m1;
y[l +32] = d2 * (q_vec[l] >> 4) - m2;
}
#else
const int tid = item_ct1.get_local_id(2);

44
ggml/src/ggml-sycl/dmmv.cpp
View File

@ -76,7 +76,7 @@ static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat *
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -104,7 +104,7 @@ static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y,
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols,
nrows, item_ct1);
});
@ -227,7 +227,7 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -346,7 +346,7 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -499,7 +499,7 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -633,7 +633,7 @@ static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -748,7 +748,7 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -774,7 +774,7 @@ static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y,
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec<QK4_0, QR4_0, dequantize_q4_0>(
vx, y, dst, ncols, nrows, item_ct1);
});
@ -795,7 +795,7 @@ static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y,
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec<QK4_1, QR4_1, dequantize_q4_1>(
vx, y, dst, ncols, nrows, item_ct1);
});
@ -816,7 +816,7 @@ static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y,
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec<QK5_0, QR5_0, dequantize_q5_0>(
vx, y, dst, ncols, nrows, item_ct1);
});
@ -837,7 +837,7 @@ static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y,
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec<QK5_1, QR5_1, dequantize_q5_1>(
vx, y, dst, ncols, nrows, item_ct1);
});
@ -858,7 +858,7 @@ static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y,
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec<QK8_0, QR8_0, dequantize_q8_0>(
vx, y, dst, ncols, nrows, item_ct1);
});
@ -873,10 +873,10 @@ static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y,
const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, 32);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1);
});
}
@ -889,10 +889,10 @@ static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y,
const int ny = 2 / K_QUANTS_PER_ITERATION;
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, 32);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1);
});
}
@ -905,10 +905,10 @@ static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y,
const int ny = 2 / K_QUANTS_PER_ITERATION;
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, 32);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1);
});
}
@ -918,10 +918,10 @@ static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y,
const int nrows,
dpct::queue_ptr stream) {
GGML_ASSERT(ncols % QK_K == 0);
const sycl::range<3> block_dims(1, 1, 32);
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
stream->parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1);
});
}
@ -934,10 +934,10 @@ static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y,
const int ny = 2 / K_QUANTS_PER_ITERATION;
const int block_num_y = (nrows + ny - 1) / ny;
const sycl::range<3> block_nums(1, 1, block_num_y);
const sycl::range<3> block_dims(1, ny, 32);
const sycl::range<3> block_dims(1, ny, WARP_SIZE);
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] {
[=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(WARP_SIZE)]] {
dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1);
});
}

6
ggml/src/ggml-sycl/dpct/helper.hpp
View File

@ -255,7 +255,7 @@ namespace dpct
void set_pitch(size_t pitch) { _pitch = pitch; }
size_t get_x() { return _x; }
void set_x(size_t x) { _x = x; };
void set_x(size_t x) { _x = x; }
size_t get_y() { return _y; }
void set_y(size_t y) { _y = y; }
@ -1056,7 +1056,7 @@ namespace dpct
#error "Only support Windows and Linux."
#endif
next_free = mapped_address_space;
};
}
public:
using buffer_id_t = int;
@ -1077,7 +1077,7 @@ namespace dpct
#else
#error "Only support Windows and Linux."
#endif
};
}
mem_mgr(const mem_mgr &) = delete;
mem_mgr &operator=(const mem_mgr &) = delete;

125
ggml/src/ggml-sycl/mmvq.cpp
View File

@ -37,7 +37,7 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -85,7 +85,7 @@ static void mul_mat_vec_q_iq2_xxs_q8_1(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -133,7 +133,7 @@ static void mul_mat_vec_q_iq2_xs_q8_1(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -181,7 +181,7 @@ static void mul_mat_vec_q_iq2_s_q8_1(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -229,7 +229,7 @@ static void mul_mat_vec_q_iq3_xxs_q8_1(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -277,7 +277,7 @@ static void mul_mat_vec_q_iq3_s_q8_1(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -325,7 +325,7 @@ static void mul_mat_vec_q_iq1_s_q8_1(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -373,7 +373,7 @@ static void mul_mat_vec_q_iq1_m_q8_1(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -421,7 +421,7 @@ static void mul_mat_vec_q_iq4_nl_q8_1(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -470,7 +470,7 @@ static void mul_mat_vec_q_iq4_xs_q8_1(const void *__restrict__ vx,
// sum up partial sums and write back result
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
tmp +=
dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask);
}
@ -495,7 +495,7 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK4_0, QI4_0, block_q4_0,
VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
@ -519,7 +519,7 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK4_0, QI4_1, block_q4_1,
VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
@ -543,7 +543,7 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK5_0, QI5_0, block_q5_0,
VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
@ -567,7 +567,7 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK5_1, QI5_1, block_q5_1,
VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
@ -591,7 +591,7 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK8_0, QI8_0, block_q8_0,
VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
@ -615,7 +615,7 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI2_K, block_q2_K,
VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
@ -639,7 +639,7 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI3_K, block_q3_K,
VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
@ -663,7 +663,7 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI4_K, block_q4_K,
VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
@ -687,7 +687,7 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI5_K, block_q5_K,
VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
@ -711,7 +711,7 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q<QK_K, QI6_K, block_q6_K,
VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>(
vx, vy, dst, ncols, nrows, item_ct1);
@ -734,8 +734,8 @@ static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS, block_iq2_xxs, 1>(
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq2_xxs_q8_1<QK_K, QI2_XXS/2, block_iq2_xxs, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -759,8 +759,8 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS, block_iq2_xs, 1>(
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq2_xs_q8_1<QK_K, QI2_XS/2, block_iq2_xs, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -784,8 +784,8 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S, block_iq2_s, 1>(
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq2_s_q8_1<QK_K, QI2_S/2, block_iq2_s, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -809,8 +809,8 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS, block_iq3_xxs, 1>(
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq3_xxs_q8_1<QK_K, QI3_XXS/2, block_iq3_xxs, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -833,8 +833,8 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_XS, block_iq3_s, 1>(
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq3_s_q8_1<QK_K, QI3_S/2, block_iq3_s, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -858,7 +858,7 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq1_s_q8_1<QK_K, QI1_S, block_iq1_s, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
@ -879,7 +879,7 @@ static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq1_m_q8_1<QK_K, QI1_S, block_iq1_m, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
@ -901,7 +901,7 @@ static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq4_nl_q8_1<QK4_NL, QI4_NL, block_iq4_nl, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
@ -923,8 +923,8 @@ static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy,
cgh.parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(32)]] {
mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS, block_iq4_xs, 1>(
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
mul_mat_vec_q_iq4_xs_q8_1<QK_K, QI4_XS/4, block_iq4_xs, 1>(
vx, vy, dst, ncols, nrows, item_ct1);
});
});
@ -936,7 +936,7 @@ void ggml_sycl_op_mul_mat_vec_q(
const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i,
float *dst_dd_i, const int64_t row_low, const int64_t row_high,
const int64_t src1_ncols, const int64_t src1_padded_row_size,
const int64_t src1_ncols, const int64_t src1_padded_col_size,
const dpct::queue_ptr &stream) {
const int64_t ne10 = src1->ne[0];
@ -948,77 +948,80 @@ void ggml_sycl_op_mul_mat_vec_q(
int id;
SYCL_CHECK(
CHECK_TRY_ERROR(id = get_current_device_id()));
const size_t q8_1_ts = sizeof(block_q8_1);
const size_t q8_1_bs = QK8_1;
// the main device has a larger memory buffer to hold the results from all GPUs
// nrows_dst == nrows of the matrix that the kernel writes into
const int64_t nrows_dst = id == ctx.device ? ne00 : row_diff;
switch (src0->type) {
for (int i = 0; i < src1_ncols; i++)
{
const size_t src1_ddq_i_offset = i * src1_padded_col_size * q8_1_ts / q8_1_bs;
const char* src1_ddq_i_bs = src1_ddq_i + src1_ddq_i_offset;
float* dst_dd_i_bs = dst_dd_i + i * dst->ne[0];
switch (src0->type) {
case GGML_TYPE_Q4_0:
mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_Q4_1:
mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_Q5_0:
mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_Q5_1:
mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_Q8_0:
mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_Q2_K:
mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_Q3_K:
mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_Q4_K:
mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_Q5_K:
mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_Q6_K:
mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_IQ1_S:
mul_mat_vec_iq1_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_iq1_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_IQ1_M:
mul_mat_vec_iq1_m_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_iq1_m_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_IQ2_XXS:
mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_iq2_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_IQ2_XS:
mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_iq2_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_IQ2_S:
mul_mat_vec_iq2_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_iq2_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_IQ3_XXS:
mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_iq3_xxs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_IQ3_S:
mul_mat_vec_iq3_s_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_iq3_s_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_IQ4_NL:
mul_mat_vec_iq4_nl_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_iq4_nl_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
case GGML_TYPE_IQ4_XS:
mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream);
mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
break;
default:
GGML_ASSERT(false);
break;
}
}
(void) src1;
(void) dst;
(void) src1_ddf_i;
(void) src1_ncols;
(void) src1_padded_row_size;
}

370
ggml/src/ggml-sycl/norm.cpp Normal file
View File

@ -0,0 +1,370 @@
#include "norm.hpp"
static void norm_f32(const float* x, float* dst, const int ncols, const float eps,
const sycl::nd_item<3>& item_ct1, sycl::float2* s_sum, int block_size) {
const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
item_ct1.get_local_id(1);
const int tid = item_ct1.get_local_id(2);
const int nthreads = item_ct1.get_local_range(2);
const int nwarps = nthreads / WARP_SIZE;
assert(nwarps % WARP_SIZE == 0);
sycl::float2 mean_var = sycl::float2(0.f, 0.f);
for (int col = tid; col < ncols; col += block_size) {
const float xi = x[row * ncols + col];
mean_var.x() += xi;
mean_var.y() += xi * xi;
}
// sum up partial sums
mean_var = warp_reduce_sum(mean_var, item_ct1);
if (block_size > WARP_SIZE) {
int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
if (lane_id == 0) {
s_sum[warp_id] = mean_var;
}
/*
DPCT1118:0: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
item_ct1.barrier(sycl::access::fence_space::local_space);
mean_var = 0.f;
int nreduce = nwarps / WARP_SIZE;
for (size_t i = 0; i < nreduce; i += 1)
{
mean_var += s_sum[lane_id + i * WARP_SIZE];
}
mean_var = warp_reduce_sum(mean_var, item_ct1);
}
const float mean = mean_var.x() / ncols;
const float var = mean_var.y() / ncols - mean * mean;
const float inv_std = sycl::rsqrt(var + eps);
for (int col = tid; col < ncols; col += block_size) {
dst[row * ncols + col] = (x[row * ncols + col] - mean) * inv_std;
}
}
static void group_norm_f32(const float* x, float* dst, const int group_size, const int ne_elements, const float eps,
const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
int start = item_ct1.get_group(2) * group_size;
int end = start + group_size;
const int nthreads = item_ct1.get_local_range(2);
const int nwarps = nthreads / WARP_SIZE;
assert(nwarps % WARP_SIZE == 0);
start += item_ct1.get_local_id(2);
if (end >= ne_elements) {
end = ne_elements;
}
float tmp = 0.0f; // partial sum for thread in warp
for (int j = start; j < end; j += block_size) {
tmp += x[j];
}
tmp = warp_reduce_sum(tmp, item_ct1);
if (block_size > WARP_SIZE) {
int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
if (lane_id == 0) {
s_sum[warp_id] = tmp;
}
/*
DPCT1118:1: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
/*
DPCT1065:54: Consider replacing sycl::nd_item::barrier() with
sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
better performance if there is no access to global memory.
*/
item_ct1.barrier();
tmp = 0.f;
int nreduce = nwarps / WARP_SIZE;
for (size_t i = 0; i < nreduce; i += 1)
{
tmp += s_sum[lane_id + i * WARP_SIZE];
}
tmp = warp_reduce_sum(tmp, item_ct1);
}
float mean = tmp / group_size;
tmp = 0.0f;
for (int j = start; j < end; j += block_size) {
float xi = x[j] - mean;
dst[j] = xi;
tmp += xi * xi;
}
tmp = warp_reduce_sum(tmp, item_ct1);
if (block_size > WARP_SIZE) {
int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
if (lane_id == 0) {
s_sum[warp_id] = tmp;
}
/*
DPCT1118:2: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
/*
DPCT1065:55: Consider replacing sycl::nd_item::barrier() with
sycl::nd_item::barrier(sycl::access::fence_space::local_space) for
better performance if there is no access to global memory.
*/
item_ct1.barrier();
tmp = s_sum[lane_id];
tmp = warp_reduce_sum(tmp, item_ct1);
}
float variance = tmp / group_size;
float scale = sycl::rsqrt(variance + eps);
for (int j = start; j < end; j += block_size) {
dst[j] *= scale;
}
}
static void rms_norm_f32(const float* x, float* dst, const int ncols, const float eps,
const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) +
item_ct1.get_local_id(1);
const int tid = item_ct1.get_local_id(2);
const int nthreads = item_ct1.get_local_range(2);
const int nwarps = nthreads / WARP_SIZE;
assert(nwarps % WARP_SIZE == 0);
float tmp = 0.0f; // partial sum for thread in warp
for (int col = tid; col < ncols; col += block_size) {
const float xi = x[row * ncols + col];
tmp += xi * xi;
}
// sum up partial sums
tmp = warp_reduce_sum(tmp, item_ct1);
if (block_size > WARP_SIZE) {
int warp_id = item_ct1.get_local_id(2) / WARP_SIZE;
int lane_id = item_ct1.get_local_id(2) % WARP_SIZE;
if (lane_id == 0) {
s_sum[warp_id] = tmp;
}
/*
DPCT1118:3: SYCL group functions and algorithms must be encountered in
converged control flow. You may need to adjust the code.
*/
item_ct1.barrier(sycl::access::fence_space::local_space);
int nreduce = nwarps / WARP_SIZE;
tmp = 0.f;
for (size_t i = 0; i < nreduce; i += 1)
{
tmp += s_sum[lane_id + i * WARP_SIZE];
}
tmp = warp_reduce_sum(tmp, item_ct1);
}
const float mean = tmp / ncols;
const float scale = sycl::rsqrt(mean + eps);
for (int col = tid; col < ncols; col += block_size) {
dst[row * ncols + col] = scale * x[row * ncols + col];
}
}
static void norm_f32_sycl(const float* x, float* dst, const int ncols,
const int nrows, const float eps,
queue_ptr stream) {
GGML_ASSERT(ncols % WARP_SIZE == 0);
if (ncols < 1024) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
stream->submit([&](sycl::handler& cgh) {
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
norm_f32(x, dst, ncols, eps, item_ct1,
nullptr, WARP_SIZE);
});
});
}
else {
const int work_group_size = get_work_group_size(stream->get_device());
const sycl::range<3> block_dims(1, 1, work_group_size);
/*
DPCT1049:17: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->submit([&](sycl::handler& cgh) {
sycl::local_accessor<sycl::float2, 1> s_sum_acc_ct1(
sycl::range<1>(work_group_size / WARP_SIZE), cgh);
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
norm_f32(x, dst, ncols, eps, item_ct1,
s_sum_acc_ct1.get_pointer(), work_group_size);
});
});
}
}
static void group_norm_f32_sycl(const float* x, float* dst,
const int num_groups, const int group_size,
const int ne_elements, queue_ptr stream) {
static const float eps = 1e-6f;
if (group_size < 1024) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
stream->submit([&](sycl::handler& cgh) {
const float eps_ct4 = eps;
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
group_norm_f32(
x, dst, group_size, ne_elements, eps_ct4, item_ct1,
nullptr, WARP_SIZE);
});
});
}
else {
const int work_group_size = get_work_group_size(stream->get_device());
const sycl::range<3> block_dims(1, 1, work_group_size);
/*
DPCT1049:18: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->submit([&](sycl::handler& cgh) {
sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
cgh);
const float eps_ct4 = eps;
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
group_norm_f32(x, dst, group_size, ne_elements,
eps_ct4, item_ct1,
s_sum_acc_ct1.get_pointer(), work_group_size);
});
});
}
}
static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols,
const int nrows, const float eps,
queue_ptr stream) {
GGML_ASSERT(ncols % WARP_SIZE == 0);
// printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE);
if (ncols < 1024) {
const sycl::range<3> block_dims(1, 1, WARP_SIZE);
stream->submit([&](sycl::handler& cgh) {
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
rms_norm_f32(x, dst, ncols, eps, item_ct1,
nullptr, WARP_SIZE);
});
});
}
else {
const int work_group_size = get_work_group_size(stream->get_device());
const sycl::range<3> block_dims(1, 1, work_group_size);
/*
DPCT1049:19: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->submit([&](sycl::handler& cgh) {
sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE),
cgh);
cgh.parallel_for(
sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims,
block_dims),
[=](sycl::nd_item<3> item_ct1)
[[intel::reqd_sub_group_size(WARP_SIZE)]] {
rms_norm_f32(x, dst, ncols, eps, item_ct1,
s_sum_acc_ct1.get_pointer(), work_group_size);
});
});
}
}
void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, const ggml_tensor* src1,
ggml_tensor* dst, const float* src0_dd,
const float* src1_dd, float* dst_dd,
const queue_ptr& main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
const int64_t ne00 = src0->ne[0];
const int64_t nrows = ggml_nrows(src0);
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
(void)src1;
(void)dst;
(void)src1_dd;
}
void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
const ggml_tensor* src1, ggml_tensor* dst,
const float* src0_dd, const float* src1_dd,
float* dst_dd,
const queue_ptr& main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
int num_groups = dst->op_params[0];
int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups);
group_norm_f32_sycl(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream);
(void)src1;
(void)dst;
(void)src1_dd;
}
void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
const ggml_tensor* src1, ggml_tensor* dst,
const float* src0_dd, const float* src1_dd,
float* dst_dd,
const queue_ptr& main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
const int64_t ne00 = src0->ne[0];
const int64_t nrows = ggml_nrows(src0);
float eps;
memcpy(&eps, dst->op_params, sizeof(float));
rms_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream);
(void)src1;
(void)dst;
(void)src1_dd;
}

35
ggml/src/ggml-sycl/norm.hpp Normal file
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@ -0,0 +1,35 @@
//
// MIT license
// Copyright (C) 2024 Intel Corporation
// SPDX-License-Identifier: MIT
//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
#ifndef GGML_SYCL_NORM_HPP
#define GGML_SYCL_NORM_HPP
#include "common.hpp"
void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0, const ggml_tensor* src1,
ggml_tensor* dst, const float* src0_dd,
const float* src1_dd, float* dst_dd,
const queue_ptr& main_stream);
void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
const ggml_tensor* src1, ggml_tensor* dst,
const float* src0_dd, const float* src1_dd,
float* dst_dd,
const queue_ptr& main_stream);
void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, const ggml_tensor* src0,
const ggml_tensor* src1, ggml_tensor* dst,
const float* src0_dd, const float* src1_dd,
float* dst_dd,
const queue_ptr& main_stream);
#endif // GGML_SYCL_NORM_HPP

2
ggml/src/ggml-sycl/presets.hpp
View File

@ -16,7 +16,7 @@
#define GGML_SYCL_MAX_STREAMS 8
#define GGML_SYCL_MAX_BUFFERS 256
#define WARP_SIZE 32
#define WARP_SIZE GGML_SYCL_WARP_SIZE
#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses
#define SYCL_GELU_BLOCK_SIZE 256

275
ggml/src/ggml-sycl/rope.cpp Normal file
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@ -0,0 +1,275 @@
#include "rope.hpp"
struct rope_corr_dims {
float v[2];
};
static float rope_yarn_ramp(const float low, const float high, const int i0) {
const float y = (i0 / 2 - low) / sycl::max(0.001f, high - low);
return 1.0f - sycl::min(1.0f, sycl::max(0.0f, y));
}
// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn
// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng.
static void rope_yarn(
float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale,
float * cos_theta, float * sin_theta) {
// Get n-d rotational scaling corrected for extrapolation
float theta_interp = freq_scale * theta_extrap;
float theta = theta_interp;
if (ext_factor != 0.0f) {
float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor;
theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
// Get n-d magnitude scaling corrected for interpolation
mscale *= 1.0f + 0.1f * sycl::log(1.0f / freq_scale);
}
*cos_theta = sycl::cos(theta) * mscale;
*sin_theta = sycl::sin(theta) * mscale;
}
template<typename T, bool has_ff>
static void rope_norm(
const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors,
const sycl::nd_item<3> &item_ct1) {
const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
item_ct1.get_local_id(1));
if (i0 >= ne0) {
return;
}
const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
item_ct1.get_local_id(2);
if (i0 >= n_dims) {
const int i = row*ne0 + i0;
dst[i + 0] = x[i + 0];
dst[i + 1] = x[i + 1];
return;
}
const int i = row*ne0 + i0;
const int i2 = row/p_delta_rows;
const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
float cos_theta;
float sin_theta;
rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
const float x0 = x[i + 0];
const float x1 = x[i + 1];
dst[i + 0] = x0*cos_theta - x1*sin_theta;
dst[i + 1] = x0*sin_theta + x1*cos_theta;
}
template<typename T, bool has_ff>
static void rope_neox(
const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows,
float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors,
const sycl::nd_item<3> &item_ct1) {
const int i0 = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) +
item_ct1.get_local_id(1));
if (i0 >= ne0) {
return;
}
const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) +
item_ct1.get_local_id(2);
if (i0 >= n_dims) {
const int i = row*ne0 + i0;
dst[i + 0] = x[i + 0];
dst[i + 1] = x[i + 1];
return;
}
const int i = row*ne0 + i0/2;
const int i2 = row/p_delta_rows;
const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f);
const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
float cos_theta;
float sin_theta;
rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta);
const float x0 = x[i + 0];
const float x1 = x[i + n_dims/2];
dst[i + 0] = x0*cos_theta - x1*sin_theta;
dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta;
}
template <typename T>
static void rope_norm_sycl(
const T *x, T *dst, int ne0, int n_dims, int nr, const int32_t *pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, queue_ptr stream) {
GGML_ASSERT(ne0 % 2 == 0);
const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ne0 + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
const sycl::range<3> block_nums(1, num_blocks_x, nr);
const float theta_scale = powf(freq_base, -2.0f/n_dims);
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
if (freq_factors == nullptr) {
/*
DPCT1049:40: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_norm<T, false>(x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows,
ext_factor, attn_factor, corr_dims, theta_scale, freq_factors,
item_ct1);
});
} else {
/*
DPCT1049:41: The work-group size passed to the SYCL kernel may exceed
the limit. To get the device limit, query
info::device::max_work_group_size. Adjust the work-group size if needed.
*/
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_norm<T, true>(x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows,
ext_factor, attn_factor, corr_dims, theta_scale, freq_factors,
item_ct1);
});
}
}
template <typename T>
static void rope_neox_sycl(
const T *x, T *dst, int ne0, int n_dims, int nr, const int32_t *pos, float freq_scale, int p_delta_rows,
float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, queue_ptr stream) {
GGML_ASSERT(ne0 % 2 == 0);
const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1);
const int num_blocks_x = (ne0 + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE);
const sycl::range<3> block_nums(1, num_blocks_x, nr);
const float theta_scale = powf(freq_base, -2.0f/n_dims);
dpct::has_capability_or_fail(stream->get_device(),
{sycl::aspect::fp16});
if (freq_factors == nullptr) {
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, false>(x, dst, ne0, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, freq_factors,
item_ct1);
});
} else {
stream->parallel_for(
sycl::nd_range<3>(block_nums * block_dims, block_dims),
[=](sycl::nd_item<3> item_ct1) {
rope_neox<T, true>(x, dst, ne0, n_dims, pos, freq_scale,
p_delta_rows, ext_factor, attn_factor,
corr_dims, theta_scale, freq_factors,
item_ct1);
});
}
}
void ggml_sycl_op_rope(
ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
const float *src0_dd, const float *src1_dd, float *dst_dd, const queue_ptr &main_stream) {
const ggml_tensor * src2 = dst->src[2];
GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16);
GGML_ASSERT(src0->type == dst->type);
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t nr = ggml_nrows(src0);
//const int n_past = ((int32_t *) dst->op_params)[0];
const int n_dims = ((int32_t *) dst->op_params)[1];
const int mode = ((int32_t *) dst->op_params)[2];
//const int n_ctx = ((int32_t *) dst->op_params)[3];
const int n_ctx_orig = ((int32_t *) dst->op_params)[4];
// RoPE alteration for extended context
float freq_base;
float freq_scale;
float ext_factor;
float attn_factor;
float beta_fast;
float beta_slow;
memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float));
memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float));
memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float));
memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float));
memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float));
const bool is_neox = mode & 2;
const int32_t * pos = (const int32_t *) src1_dd;
const float * freq_factors = nullptr;
if (src2 != nullptr) {
freq_factors = (const float *) src2->data;
}
rope_corr_dims corr_dims;
ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims.v);
// compute
if (is_neox) {
if (src0->type == GGML_TYPE_F32) {
rope_neox_sycl(
(const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, freq_factors, main_stream
);
} else if (src0->type == GGML_TYPE_F16) {
rope_neox_sycl(
(const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, freq_factors, main_stream
);
} else {
GGML_ASSERT(false);
}
} else {
if (src0->type == GGML_TYPE_F32) {
rope_norm_sycl(
(const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, freq_factors, main_stream
);
} else if (src0->type == GGML_TYPE_F16) {
rope_norm_sycl(
(const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor,
attn_factor, corr_dims, freq_factors, main_stream
);
} else {
GGML_ASSERT(false);
}
}
(void) src1;
(void) dst;
(void) src1_dd;
}

22
ggml/src/ggml-sycl/rope.hpp Normal file
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@ -0,0 +1,22 @@
//
// MIT license
// Copyright (C) 2024 Intel Corporation
// SPDX-License-Identifier: MIT
//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
#ifndef GGML_SYCL_ROPE_HPP
#define GGML_SYCL_ROPE_HPP
#include "common.hpp"
void ggml_sycl_op_rope(
ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst,
const float *src0_dd, const float *src1_dd, float *dst_dd, const queue_ptr &main_stream);
#endif // GGML_SYCL_ROPE_HPP

21
ggml/src/ggml-sycl/vecdotq.hpp
View File

@ -820,7 +820,6 @@ vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq,
#if QK_K == 256
const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq;
#if QR2_XXS == 8
const int ib32 = iqs;
const uint16_t * q2 = bq2->qs + 4*ib32;
const uint8_t * aux8 = (const uint8_t *)q2;
@ -838,26 +837,6 @@ vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq,
}
const float d = (float)bq2->d * (0.5f + aux32) * bq8_1[ib32].ds[0] * 0.25f;
return d * sumi;
#else
// iqs is 0...15
const int ib32 = iqs/2;
const int il = iqs%2;
const uint16_t * q2 = bq2->qs + 4*ib32;
const uint8_t * aux8 = (const uint8_t *)q2;
const uint8_t * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]);
const uint8_t * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]);
const uint32_t aux32 = q2[2] | (q2[3] << 16);
const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * bq8_1[ib32].ds[0] * 0.25f;
const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127];
const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127];
const int8_t * q8 = bq8_1[ib32].qs + 16*il;
int sumi1 = 0, sumi2 = 0;
for (int j = 0; j < 8; ++j) {
sumi1 += q8[j+0] * grid1[j] * (signs1 & kmask_iq2xs[j] ? -1 : 1);
sumi2 += q8[j+8] * grid2[j] * (signs2 & kmask_iq2xs[j] ? -1 : 1);
}
return d * (sumi1 + sumi2);
#endif
#else
assert(false);
return 0.f;

1
ggml/src/ggml-vulkan-shaders.hpp
View File

@ -144954,4 +144954,3 @@ unsigned char sum_rows_f32_data[] = {
};
const uint64_t sum_rows_f32_len = 2112;

2
ggml/src/ggml.c
View File

@ -5312,7 +5312,7 @@ void ggml_mul_mat_set_prec(
as -> [cols, rows, n_expert]
ids -> [n_experts_used, n_tokens] (i32)
b -> [cols, n_expert_used, n_tokens]
c -> [cols, n_expert_used, n_tokens]
c -> [rows, n_expert_used, n_tokens]
in b, n_experts_used can be broadcasted to match the n_expert_used of ids

30
gguf-py/gguf/constants.py
View File

@ -66,6 +66,7 @@ class Keys:
Q_LORA_RANK = "{arch}.attention.q_lora_rank"
KV_LORA_RANK = "{arch}.attention.kv_lora_rank"
REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count"
SLIDING_WINDOW = "{arch}.attention.sliding_window"
class Rope:
DIMENSION_COUNT = "{arch}.rope.dimension_count"
@ -160,10 +161,12 @@ class MODEL_ARCH(IntEnum):
COMMAND_R = auto()
DBRX = auto()
OLMO = auto()
OPENELM = auto()
ARCTIC = auto()
DEEPSEEK2 = auto()
BITNET = auto()
T5 = auto()
JAIS = auto()
class MODEL_TENSOR(IntEnum):
@ -288,10 +291,12 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.COMMAND_R: "command-r",
MODEL_ARCH.DBRX: "dbrx",
MODEL_ARCH.OLMO: "olmo",
MODEL_ARCH.OPENELM: "openelm",
MODEL_ARCH.ARCTIC: "arctic",
MODEL_ARCH.DEEPSEEK2: "deepseek2",
MODEL_ARCH.BITNET: "bitnet",
MODEL_ARCH.T5: "t5",
MODEL_ARCH.JAIS: "jais",
}
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -894,6 +899,19 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.OPENELM: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K_NORM,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.ARCTIC: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
@ -989,6 +1007,18 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.ENC_FFN_UP,
MODEL_TENSOR.ENC_OUTPUT_NORM,
],
MODEL_ARCH.JAIS: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_UP,
],
# TODO
}

17
gguf-py/gguf/gguf_writer.py
View File

@ -480,8 +480,11 @@ class GGUFWriter:
def add_leading_dense_block_count(self, length: int) -> None:
self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length)
def add_feed_forward_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_feed_forward_length(self, length: int | Sequence[int]) -> None:
if isinstance(length, int):
self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
else:
self.add_array(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_expert_feed_forward_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
@ -495,8 +498,11 @@ class GGUFWriter:
def add_decoder_start_token_id(self, id: int) -> None:
self.add_uint32(Keys.LLM.DECODER_START_TOKEN_ID.format(arch=self.arch), id)
def add_head_count(self, count: int) -> None:
self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
def add_head_count(self, count: int | Sequence[int]) -> None:
if isinstance(count, int):
self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
else:
self.add_array(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
def add_head_count_kv(self, count: int | Sequence[int]) -> None:
if isinstance(count, int):
@ -555,6 +561,9 @@ class GGUFWriter:
def add_relative_attn_buckets_count(self, value: int) -> None:
self.add_uint32(Keys.Attention.REL_BUCKETS_COUNT.format(arch=self.arch), value)
def add_sliding_window(self, value: int) -> None:
self.add_uint32(Keys.Attention.SLIDING_WINDOW.format(arch=self.arch), value)
def add_pooling_type(self, value: PoolingType) -> None:
self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value)

35
gguf-py/gguf/tensor_mapping.py
View File

@ -10,7 +10,7 @@ class TensorNameMap:
# Token embeddings
MODEL_TENSOR.TOKEN_EMBD: (
"gpt_neox.embed_in", # gptneox
"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx
"transformer.wte", # gpt2 gpt-j mpt refact qwen dbrx jais
"transformer.word_embeddings", # falcon
"word_embeddings", # bloom
"model.embed_tokens", # llama-hf
@ -24,6 +24,7 @@ class TensorNameMap:
"backbone.embedding", # mamba
"backbone.embeddings", # mamba-hf
"transformer.in_out_embed", # Grok
"transformer.token_embeddings", # openelm
"shared", # t5
),
@ -37,6 +38,7 @@ class TensorNameMap:
"word_embeddings_layernorm", # bloom
"embeddings.LayerNorm", # bert
"emb_ln", # nomic-bert
"transformer.norm", # openelm
),
# Position embeddings
@ -49,7 +51,7 @@ class TensorNameMap:
# Output
MODEL_TENSOR.OUTPUT: (
"embed_out", # gptneox
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx jais
"output", # llama-pth bloom internlm2
"word_embeddings_for_head", # persimmon
"lm_head.linear", # phi2
@ -58,7 +60,7 @@ class TensorNameMap:
# Output norm
MODEL_TENSOR.OUTPUT_NORM: (
"gpt_neox.final_layer_norm", # gptneox
"transformer.ln_f", # gpt2 gpt-j falcon
"transformer.ln_f", # gpt2 gpt-j falcon jais
"model.norm", # llama-hf baichuan internlm2
"norm", # llama-pth
"transformer.norm_f", # mpt dbrx
@ -69,6 +71,7 @@ class TensorNameMap:
"model.norm_f", # mamba-qbert
"backbone.norm_f", # mamba
"transformer.rms_norm", # Grok
"transformer.norm", # openelm
),
# Rope frequencies
@ -81,7 +84,7 @@ class TensorNameMap:
# Attention norm
MODEL_TENSOR.ATTN_NORM: (
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen jais
"transformer.blocks.{bid}.norm_1", # mpt
"transformer.h.{bid}.input_layernorm", # falcon7b
"h.{bid}.input_layernorm", # bloom
@ -98,6 +101,7 @@ class TensorNameMap:
"backbone.layers.{bid}.norm", # mamba
"transformer.decoder_layer.{bid}.rms_norm", # Grok
"transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx
"transformer.layers.{bid}.attn_norm", # openelm
),
# Attention norm 2
@ -109,7 +113,7 @@ class TensorNameMap:
# Attention query-key-value
MODEL_TENSOR.ATTN_QKV: (
"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
"transformer.h.{bid}.attn.c_attn", # gpt2 qwen
"transformer.h.{bid}.attn.c_attn", # gpt2 qwen jais
"transformer.blocks.{bid}.attn.Wqkv", # mpt
"transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv", # dbrx
"transformer.h.{bid}.self_attention.query_key_value", # falcon
@ -119,7 +123,8 @@ class TensorNameMap:
"h.{bid}.attn.c_attn", # gpt2
"transformer.h.{bid}.mixer.Wqkv", # phi2
"encoder.layers.{bid}.attn.Wqkv", # nomic-bert
"model.layers.{bid}.self_attn.qkv_proj" # phi3
"model.layers.{bid}.self_attn.qkv_proj", # phi3
"transformer.layers.{bid}.attn.qkv_proj", # openelm
),
# Attention query
@ -160,7 +165,7 @@ class TensorNameMap:
# Attention output
MODEL_TENSOR.ATTN_OUT: (
"gpt_neox.layers.{bid}.attention.dense", # gptneox
"transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen
"transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen jais
"transformer.blocks.{bid}.attn.out_proj", # mpt
"transformer.h.{bid}.self_attention.dense", # falcon
"h.{bid}.self_attention.dense", # bloom
@ -177,6 +182,7 @@ class TensorNameMap:
"encoder.layers.{bid}.attn.out_proj", # nomic-bert
"transformer.decoder_layer.{bid}.multi_head_attention.linear", # Grok
"transformer.blocks.{bid}.norm_attn_norm.attn.out_proj", # dbrx
"transformer.layers.{bid}.attn.out_proj", # openelm
),
# Attention output norm
@ -202,7 +208,7 @@ class TensorNameMap:
# Feed-forward norm
MODEL_TENSOR.FFN_NORM: (
"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
"transformer.h.{bid}.ln_2", # gpt2 refact qwen
"transformer.h.{bid}.ln_2", # gpt2 refact qwen jais
"h.{bid}.post_attention_layernorm", # bloom
"transformer.blocks.{bid}.norm_2", # mpt
"model.layers.{bid}.post_attention_layernorm", # llama-hf
@ -212,6 +218,7 @@ class TensorNameMap:
"h.{bid}.ln_2", # gpt2
"model.layers.{bid}.ffn_norm", # internlm2
"transformer.decoder_layer.{bid}.rms_norm_2", # Grok
"transformer.layers.{bid}.ffn_norm", # openelm
"model.layers.{bid}.pre_ff_layernorm", # jamba
"model.layers.{bid}.pre_moe_layernorm", # mini-jamba
),
@ -242,7 +249,7 @@ class TensorNameMap:
# Feed-forward up
MODEL_TENSOR.FFN_UP: (
"gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox
"transformer.h.{bid}.mlp.c_fc", # gpt2
"transformer.h.{bid}.mlp.c_fc", # gpt2 jais
"transformer.blocks.{bid}.ffn.up_proj", # mpt
"transformer.h.{bid}.mlp.dense_h_to_4h", # falcon
"h.{bid}.mlp.dense_h_to_4h", # bloom
@ -289,6 +296,7 @@ class TensorNameMap:
"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
"layers.{bid}.feed_forward.w1", # llama-pth
"transformer.h.{bid}.mlp.w2", # qwen
"transformer.h.{bid}.mlp.c_fc2", # jais
"model.layers.layers.{bid}.mlp.gate_proj", # plamo
"model.layers.{bid}.feed_forward.w1", # internlm2
"encoder.layers.{bid}.mlp.fc12", # nomic-bert
@ -313,7 +321,7 @@ class TensorNameMap:
# Feed-forward down
MODEL_TENSOR.FFN_DOWN: (
"gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
"transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen
"transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen jais
"transformer.blocks.{bid}.ffn.down_proj", # mpt
"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
"h.{bid}.mlp.dense_4h_to_h", # bloom
@ -331,6 +339,7 @@ class TensorNameMap:
"encoder.layers.{bid}.mlp.fc2", # nomic-bert
"model.layers.{bid}.mlp.c_proj", # starcoder2
"encoder.layer.{bid}.mlp.wo", # jina-bert-v2
"transformer.layers.{bid}.ffn.proj_2", # openelm
"model.layers.{bid}.residual_mlp.w2", # arctic
"encoder.layer.{bid}.mlp.down_layer", # jina-bert-v2
"model.layers.{bid}.feed_forward.down_proj", # jamba
@ -353,7 +362,8 @@ class TensorNameMap:
"model.layers.{bid}.self_attn.q_layernorm", # persimmon
"model.layers.{bid}.self_attn.q_norm", # cohere
"transformer.blocks.{bid}.attn.q_ln", # sea-lion
"encoder.layer.{bid}.attention.self.layer_norm_q" # jina-bert-v2
"encoder.layer.{bid}.attention.self.layer_norm_q", # jina-bert-v2
"transformer.layers.{bid}.attn.q_norm", # openelm
),
MODEL_TENSOR.ATTN_K_NORM: (
@ -361,7 +371,8 @@ class TensorNameMap:
"model.layers.{bid}.self_attn.k_layernorm", # persimmon
"model.layers.{bid}.self_attn.k_norm", # cohere
"transformer.blocks.{bid}.attn.k_ln", # sea-lion
"encoder.layer.{bid}.attention.self.layer_norm_k" # jina-bert-v2
"encoder.layer.{bid}.attention.self.layer_norm_k", # jina-bert-v2
"transformer.layers.{bid}.attn.k_norm", # openelm
),
MODEL_TENSOR.ROPE_FREQS: (

16
include/llama.h
View File

@ -89,6 +89,7 @@ extern "C" {
LLAMA_VOCAB_PRE_TYPE_SMAUG = 14,
LLAMA_VOCAB_PRE_TYPE_PORO = 15,
LLAMA_VOCAB_PRE_TYPE_VIKING = 16,
LLAMA_VOCAB_PRE_TYPE_JAIS = 17,
};
// note: these values should be synchronized with ggml_rope
@ -484,6 +485,13 @@ extern "C" {
// Get a llama model tensor
LLAMA_API struct ggml_tensor * llama_get_model_tensor(struct llama_model * model, const char * name);
// Returns true if the model contains an encoder that requires llama_encode() call
LLAMA_API bool llama_model_has_encoder(const struct llama_model * model);
// For encoder-decoder models, this function returns id of the token that must be provided
// to the decoder to start generating output sequence. For other models, it returns -1.
LLAMA_API llama_token llama_model_decoder_start_token(const struct llama_model * model);
// Returns 0 on success
LLAMA_API uint32_t llama_model_quantize(
const char * fname_inp,
@ -819,6 +827,14 @@ extern "C" {
// Frees a batch of tokens allocated with llama_batch_init()
LLAMA_API void llama_batch_free(struct llama_batch batch);
// Processes a batch of tokens with the ecoder part of the encoder-decoder model.
// Stores the encoder output internally for later use by the decoder cross-attention layers.
// 0 - success
// < 0 - error
LLAMA_API int32_t llama_encode(
struct llama_context * ctx,
struct llama_batch batch);
// Positive return values does not mean a fatal error, but rather a warning.
// 0 - success
// 1 - could not find a KV slot for the batch (try reducing the size of the batch or increase the context)

6
models/ggml-vocab-bert-bge.gguf.inp
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@ -73,6 +73,8 @@ __ggml_vocab_test__
__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
__ggml_vocab_test__
3
__ggml_vocab_test__
33
@ -91,6 +93,10 @@ __ggml_vocab_test__
__ggml_vocab_test__
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3
models/ggml-vocab-bert-bge.gguf.out
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@ -31,6 +31,7 @@
1027
1005 3690
7592 1010 1061 1005 2035 999 2129 2024 2017 100 1029 1855 100 100 6207 100 100 14677 23632 22203 1811 1995
999 999 999 999 999 999
1017
3943
21211
@ -40,4 +41,6 @@
21211 22394 22394
21211 22394 22394 2509
21211 22394 22394 22394
12731 2050 19710
5860 18117
100 1006 3671 1007 100 1006 3674 7861 29147 2483 9530 16280 23854 1007 100 100 1017 3943 21211 21211 2509 21211 22394 21211 22394 2509 21211 22394 22394 21211 22394 22394 2509 1017 1012 1017 1017 1012 1012 1017 1017 1012 1012 1012 1017 100 1029 1855 100 100 6207 100 100 14677 23632 22203 1811 1995 1011 1011 1011 1011 1011 1011 1027 1027 1027 1027 1027 1027 1027 1192 15290 29754 14150 1192 10260 1181 29755 29436 29741 10260 16856 29747 23925 10325 1005 1005 1005 1005 1005 1005 1036 1036 1036 1036 1036 1036 1036 1000 1000 1000 1000 1012 1012 1012 1012 1012 1012 999 999 999 999 999 999 1029 1029 1029 1029 1029 1029 1045 1005 2310 2042 1005 2409 2002 1005 1055 2045 1010 1005 2128 2017 2469 1029 1005 1049 2025 2469 1045 1005 2222 2191 2009 1010 1005 1040 2017 2066 2070 5572 1029 2057 1005 2310 1037 1005 2222

6
models/ggml-vocab-command-r.gguf.inp
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@ -73,6 +73,8 @@ __ggml_vocab_test__
__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
__ggml_vocab_test__
3
__ggml_vocab_test__
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models/ggml-vocab-command-r.gguf.out
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@ -31,6 +31,7 @@
206 1857
14 4515
28339 19 1770 14 1954 8 4070 1955 1933 80503 231 5691 12081 13336 2648 29325 14315 24 26 24 27 24 28 24 5123 18372
57178 10251
26
26 26
26 26 26
@ -40,4 +41,6 @@
26 26 26 26 26 26 26
26 26 26 26 26 26 26 26
26 26 26 26 26 26 26 26 26
42 30719 12584
3642 4388
127731 51628 205 57788 18494 97469 126134 206 2226 256 230 1737 18258 16 80503 122 35927 2226 242 112 57462 1737 54457 223165 106230 2096 16 48389 11254 107 255 2226 107 255 228 26 228 26 26 228 26 26 26 228 26 26 26 26 228 26 26 26 26 26 228 26 26 26 26 26 26 228 26 26 26 26 26 26 26 228 26 26 26 26 26 26 26 26 228 26 21 26 228 26 2271 26 228 26 3834 26 182018 230 174833 38111 249 86325 241 38111 245 86325 232 38111 252 38111 123 38111 261 165 24629 38111 261 38111 103 174833 38111 235 188568 231 5691 12081 13336 2648 29325 14315 24 26 24 27 24 28 24 5123 18372 8391 158343 3512 40071 2196 3236 8750 1764 37097 41168 29721 32797 25646 3802 4975 4975 116167 57178 10251 154048 27292 1767 5125 2632 2155 91 2378 1919 1914 2782 19 2155 3354 1933 5470 38 2155 52 2068 5470 1767 4961 3059 1894 19 2155 43 1933 3026 2725 23186 38 2930 14 20676 1671 14 83 51

6
models/ggml-vocab-deepseek-coder.gguf.inp
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@ -73,6 +73,8 @@ __ggml_vocab_test__
__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
__ggml_vocab_test__
3
__ggml_vocab_test__
33
@ -91,6 +93,10 @@ __ggml_vocab_test__
__ggml_vocab_test__
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models/ggml-vocab-deepseek-coder.gguf.out
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@ -31,6 +31,7 @@
185 405
6 2895
17535 11 320 6 435 0 1717 417 340 12394 233 210 3015 19100 608 9413 2668 16 18 16 19 16 20 16 1393 169 121 239
15330 3023
18
18 18
18 18 18
@ -40,4 +41,6 @@
18 18 18 18 18 18 18
18 18 18 18 18 18 18 18
18 18 18 18 18 18 18 18 18
34 155 119 242 64 24297 155 119 216 83
1607 2539
185 207 185 185 207 185 185 185 207 12405 459 22758 185 243 185 315 185 251 185 730 185 10047 235 209 334 8760 8 12394 233 114 350 222 10047 221 104 169 116 224 334 4684 3909 992 24330 262 29651 612 8 207 156 237 214 12394 99 234 10047 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 524 18 207 18 1202 18 207 155 239 209 155 239 114 155 239 228 155 240 220 155 239 224 155 240 211 155 239 231 155 239 115 155 239 240 155 240 210 155 239 240 155 239 95 155 239 114 155 239 214 10047 233 210 3015 19100 608 9413 2668 16 18 16 19 16 20 16 1393 169 121 239 18155 374 17194 28 2861 6478 616 2251 14994 31269 4191 6 4686 4686 10252 3358 3358 3409 524 15330 3023 15031 5668 303 6 312 798 651 83 839 362 6 82 741 11 651 1369 340 2037 30 651 44 441 2037 303 6 642 1098 359 11 651 35 340 833 738 10860 30 998 6 10709 245 6 75 43

6
models/ggml-vocab-deepseek-llm.gguf.inp
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@ -73,6 +73,8 @@ __ggml_vocab_test__
__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
__ggml_vocab_test__
3
__ggml_vocab_test__
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models/ggml-vocab-deepseek-llm.gguf.out
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@ -31,6 +31,7 @@
185 403
6 2906
17464 11 320 6 436 0 1724 418 340 33701 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239
15278 3033
18
18 18
18 18 18
@ -40,4 +41,6 @@
18 18 18 18 18 18 18
18 18 18 18 18 18 18 18
18 18 18 18 18 18 18 18 18
34 32555 242 64 23708 32555 216 83
1763 2550
185 207 185 185 207 185 185 185 207 11969 486 22504 185 243 185 300 185 251 185 663 185 10044 95300 334 8754 8 33701 114 350 222 10044 221 104 46713 334 34732 996 24250 262 80923 8 207 37103 214 12356 99 234 10044 99 234 207 18 207 18 18 207 18 18 18 207 18 18 18 18 207 18 18 18 18 18 207 18 18 18 18 18 18 207 18 18 18 18 18 18 18 207 18 18 18 18 18 18 18 18 207 18 13 18 207 18 526 18 207 18 1204 18 207 71374 209 71374 114 71374 228 155 240 220 71374 224 155 240 211 71374 231 71374 115 71374 240 155 240 210 71374 240 71374 95 71374 114 71374 214 71899 210 3025 19017 612 9407 2681 16 18 16 19 16 20 16 1398 68940 239 78827 55170 76659 620 91754 31116 36804 4885 4885 10897 4390 4390 41047 15278 3033 14986 5675 304 6 313 803 655 33326 362 6 82 745 11 655 1374 340 2049 30 655 44 441 2049 304 6 647 1099 359 11 655 35 340 837 742 10842 30 1003 6 10699 245 6 75 43

6
models/ggml-vocab-falcon.gguf.inp
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@ -73,6 +73,8 @@ __ggml_vocab_test__
__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
__ggml_vocab_test__
3
__ggml_vocab_test__
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__ggml_vocab_test__
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models/ggml-vocab-falcon.gguf.out
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@ -31,6 +31,7 @@
1212 40
18 4932
9856 23 291 18 436 12 1265 362 299 8196 207 204 42 50087 123 2727 20300 32022 133 234 17419 30137 28 7858 181 133 236
51520
30
3138
22287
@ -40,4 +41,6 @@
22287 22287 30
22287 22287 3138
22287 22287 22287
46 19768 239 76 9634 19768 213 95
1080 1502
1212 4824 1001 1212 192 204 663 49453 2069 742 561 1501 193 2571 232 206 204 19 11003 20 8196 126 283 219 48778 116 13392 204 19 51831 732 63209 1741 7955 522 20 22438 211 3346 111 231 2571 111 231 204 30 204 3138 204 22287 204 22287 30 204 22287 3138 204 22287 22287 204 22287 22287 30 204 22287 22287 3138 204 30 25 30 204 30 513 30 204 30 951 30 27171 236 206 38154 126 38154 225 167 237 217 38154 221 167 237 208 38154 228 38154 127 38154 237 167 237 207 38154 237 38154 107 38154 126 38154 211 20589 207 204 42 50087 123 2727 20300 32022 133 234 17419 30137 28 7858 181 133 236 204 37057 2228 10666 5052 133 6207 151 215 150 134 5052 133 6279 5052 223 151 216 49679 123 53110 47043 7795 204 7544 7544 7544 8543 8543 17593 3513 3513 12844 51520 17664 4247 295 18 298 650 204 18 95 693 332 18 94 629 23 204 18 1553 299 1310 42 204 18 56 416 1310 295 18 567 717 334 23 204 18 47 299 606 596 6696 42 703 18 16139 241 18 87 55

6
models/ggml-vocab-gpt-2.gguf.inp
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@ -73,6 +73,8 @@ __ggml_vocab_test__
__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
__ggml_vocab_test__
3
__ggml_vocab_test__
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@ -91,6 +93,10 @@ __ggml_vocab_test__
__ggml_vocab_test__
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models/ggml-vocab-gpt-2.gguf.out
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@ -31,6 +31,7 @@
198 796
6 6980
15496 11 331 6 439 0 1374 389 345 30325 223 5633 22755 239 46349 111 28839 101 18040 32432 98 43291 1485 1415 24309 25465 171 121 252
13896 3228
18
2091
20370
@ -40,4 +41,6 @@
24840 20370
24840 24840
24840 2091 20370
34 157 119 255 64 16049 157 119 229 83
1221 1371
198 220 628 220 628 198 220 197 220 197 197 220 197 198 220 220 198 220 220 220 198 220 220 220 220 198 220 220 220 220 220 198 8582 248 222 357 11265 8 30325 114 447 235 8582 234 104 37929 357 48101 795 13210 271 1673 36686 515 8 14519 227 12520 99 247 8582 99 247 513 4747 23460 513 20370 23460 2091 23460 20370 23460 24840 23460 2091 20370 513 13 18 513 492 18 513 986 18 28053 252 222 157 252 114 157 252 241 157 253 233 157 252 237 157 253 224 157 252 244 157 252 115 157 252 253 157 253 223 157 252 253 157 252 95 157 252 114 157 252 227 47249 223 5633 22755 239 46349 111 28839 101 18040 32432 98 43291 1485 1415 24309 25465 171 121 252 40103 1421 18604 12466 121 16843 141 231 15166 12466 121 16142 12466 239 141 232 30143 140 111 16142 21169 21727 31583 18849 705 39115 6 33153 15506 63 15931 15931 16317 13896 3228 9805 3548 314 1053 587 705 44040 339 338 612 11 705 2200 345 1654 30 705 44 407 1654 314 1183 787 340 11 705 35 345 588 617 8887 30 775 6 26979 257 6 75 43

8
models/ggml-vocab-llama-bpe.gguf.inp
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@ -73,6 +73,8 @@ __ggml_vocab_test__
__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
__ggml_vocab_test__
3
__ggml_vocab_test__
33
@ -91,6 +93,10 @@ __ggml_vocab_test__
__ggml_vocab_test__
333333333
__ggml_vocab_test__
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__ggml_vocab_test__
@ -104,5 +110,3 @@ __ggml_vocab_test__
🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL
__ggml_vocab_test__
Việt
__ggml_vocab_test__

4
models/ggml-vocab-llama-bpe.gguf.out
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@ -31,6 +31,7 @@
198 284
6 11639
9906 11 379 65948 0 2650 527 499 27623 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909
17523 3001
18
1644
8765
@ -40,5 +41,6 @@
8765 8765 18
8765 8765 1644
8765 8765 8765
34 91163 101798
2624 2402
198 4815 15073 66597 8004 1602 2355 79772 11187 9468 248 222 320 8416 8 27623 114 102470 9468 234 104 31643 320 36773 100166 98634 8 26602 227 11410 99 247 9468 99 247 220 18 220 1644 220 8765 220 8765 18 220 8765 1644 220 8765 8765 220 8765 8765 18 220 8765 8765 1644 220 18 13 18 220 18 497 18 220 18 1131 18 220 21549 222 98629 241 45358 233 21549 237 45358 224 21549 244 21549 115 21549 253 45358 223 21549 253 21549 95 98629 227 76460 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909 56560 54337 19175 102118 13373 64571 34694 3114 112203 80112 3436 106451 14196 14196 74694 3089 3089 29249 17523 3001 27708 7801 358 3077 1027 364 83 820 568 596 1070 11 364 793 499 2771 30 364 44 539 2771 358 3358 1304 433 11 364 35 499 1093 1063 15600 30 1226 6 43712 264 64966 43
101798

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models/ggml-vocab-llama-spm.gguf.inp
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@ -73,6 +73,8 @@ __ggml_vocab_test__
__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
__ggml_vocab_test__
3
__ggml_vocab_test__
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@ -91,6 +93,10 @@ __ggml_vocab_test__
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models/ggml-vocab-llama-spm.gguf.out
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@ -31,6 +31,7 @@
29871 13 353
525 3152
15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739
1738 6824 21004
29871 29941
29871 29941 29941
29871 29941 29941 29941
@ -40,4 +41,6 @@
29871 29941 29941 29941 29941 29941 29941 29941
29871 29941 29941 29941 29941 29941 29941 29941 29941
29871 29941 29941 29941 29941 29941 29941 29941 29941 29941
315 228 190 176 29874 10630 30529 29873
29871 2313 3163
29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931

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models/ggml-vocab-mpt.gguf.inp
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@ -73,6 +73,8 @@ __ggml_vocab_test__
__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
__ggml_vocab_test__
3
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187 426
8 8685
12092 13 340 8 455 2 1359 403 368 49042 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241
18963 4672
20
1610
20084
@ -40,4 +41,6 @@
26409 20084
26409 26409
26409 1610 20084
36 6829 244 66 17721 35177 85
1262 2196
586 1744 33525 186 209 623 28910 187 50276 187 50275 187 50274 187 50273 187 14931 237 211 313 6320 10 49042 116 325 224 14931 223 106 171 118 226 313 34263 802 13511 261 32147 456 10 3384 239 216 22692 101 236 14931 101 236 495 5922 30057 495 20084 495 26409 30057 20084 495 26409 1610 495 26409 20084 495 15 20 495 537 20 495 1051 20 209 18081 211 18081 116 18081 230 39936 222 18081 226 39936 213 18081 233 18081 117 18081 242 39936 212 18081 242 18081 97 18081 116 18081 216 14931 235 212 3736 15367 41197 13610 19934 41869 21275 1012 1047 18795 40120 20422 241 16081 6877 12880 11514 1068 8713 38177 13396 3415 9925 12559 10453 1389 42011 35033 34842 11202 9739 9739 33021 18963 4672 25561 8220 309 1849 644 686 42618 344 434 627 13 686 1848 368 2119 32 686 46 417 2119 309 1833 1056 352 13 686 37 368 751 690 10331 32 844 8 31516 247 8 77 45

6
models/ggml-vocab-phi-3.gguf.inp
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@ -73,6 +73,8 @@ __ggml_vocab_test__
__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
__ggml_vocab_test__
3
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models/ggml-vocab-phi-3.gguf.out
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@ -31,6 +31,7 @@
29871 13 353
525 3152
15043 29892 343 29915 497 29991 1128 526 366 29871 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739
1738 6824 21004
29871 29941
29871 29941 29941
29871 29941 29941 29941
@ -40,4 +41,6 @@
29871 29941 29941 29941 29941 29941 29941 29941
29871 29941 29941 29941 29941 29941 29941 29941 29941
29871 29941 29941 29941 29941 29941 29941 29941 29941 29941
315 228 190 176 29874 10630 30529 29873
29871 2313 3163
29871 13 29871 13 13 29871 13 13 13 29871 12 29871 12 12 29871 12 13 259 13 1678 13 268 13 418 13 243 162 157 131 313 8945 29897 29871 243 162 155 185 30722 243 162 143 174 30598 313 20787 953 3848 275 16125 630 29897 29871 31681 29871 243 162 169 156 243 162 169 156 29871 29941 29871 29941 29941 29871 29941 29941 29941 29871 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29871 29941 29941 29941 29941 29941 29941 29941 29941 29871 29941 29889 29941 29871 29941 636 29941 29871 29941 856 29941 29871 31849 31324 31934 228 162 142 228 161 146 228 162 133 228 161 153 228 161 186 31708 228 162 132 31708 228 161 165 31324 228 161 136 243 162 155 132 1577 30672 31522 30505 11548 31041 30732 29896 29941 29896 29946 29896 29945 29896 30408 30739 448 23648 2751 25512 1538 4851 665 1386 29713 1305 14550 4907 11120 16159 16159 16159 15945 15945 3045 636 6824 6824 6824 8773 8773 8773 306 29915 345 1063 525 29873 1025 540 29915 29879 727 29892 525 1525 366 1854 29973 525 29924 451 1854 306 29915 645 1207 372 29892 525 29928 366 763 777 23429 29973 1334 29915 29963 29872 263 29915 29880 29931

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models/ggml-vocab-qwen2.gguf.inp
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@ -73,6 +73,8 @@ __ggml_vocab_test__
__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
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3
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198 284
6 11385
9707 11 379 64848 0 2585 525 498 26525 223 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216
17085 2928
18
18 18
18 18 18
@ -40,4 +41,6 @@
18 18 18 18 18 18 18
18 18 18 18 18 18 18 18
18 18 18 18 18 18 18 18 18
34 90063 128324
2560 2347
198 4710 14731 65497 7847 1572 2303 78672 10947 145836 320 8252 8 26525 114 378 235 149921 30543 320 35673 99066 97534 8 25521 227 11162 99 247 149955 220 18 220 18 18 220 18 18 18 220 18 18 18 18 220 18 18 18 18 18 220 18 18 18 18 18 18 220 18 18 18 18 18 18 18 220 18 18 18 18 18 18 18 18 220 18 13 18 220 18 496 18 220 18 1112 18 220 146394 97529 241 44258 233 146568 44258 224 147603 20879 115 146280 44258 223 146280 147272 97529 227 144534 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216 55460 53237 18658 14144 1456 13073 63471 33594 3038 133178 79012 3355 4605 4605 13874 13874 73594 3014 3014 28149 17085 2928 26610 7646 358 3003 1012 364 83 813 566 594 1052 11 364 787 498 2704 30 364 44 537 2704 358 3278 1281 432 11 364 35 498 1075 1045 15243 30 1205 6 42612 264 63866 43

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models/ggml-vocab-refact.gguf.inp
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__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
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203 280
25 34666
8279 30 533 25 464 19 4971 884 844 18458 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838
9163 3202
37
37 37
37 37 37
@ -40,4 +41,6 @@
37 37 37 37 37 37 37
37 37 37 37 37 37 37 37
37 37 37 37 37 37 37 37 37
53 33934 83 33217 17102 102
1214 12258
334 719 8878 202 10885 4222 16104 28570 203 3807 253 227 308 4382 27 18458 133 46113 44967 123 13868 308 12565 19775 33071 40824 733 27 41889 5945 118 252 3807 118 252 225 37 225 37 37 225 37 37 37 225 37 37 37 37 225 37 37 37 37 37 225 37 37 37 37 37 37 225 37 37 37 37 37 37 37 225 37 37 37 37 37 37 37 37 225 37 32 37 225 37 497 37 225 37 1179 37 225 14574 227 14574 133 14574 246 30457 238 14574 242 30457 229 14574 249 14574 134 14574 258 30457 228 14574 258 14574 114 14574 133 14574 232 36628 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838 20921 16623 13028 8372 1039 9446 40242 13852 2053 8949 12531 1520 10700 5881 9592 13299 914 31753 31359 9163 3202 35472 10397 439 4763 2583 330 102 1455 938 1182 2017 30 330 613 844 3654 49 330 63 646 3654 439 4621 1930 561 30 330 54 844 2124 1629 35993 49 2688 25 7709 312 25 94 62

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models/ggml-vocab-starcoder.gguf.inp
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@ -73,6 +73,8 @@ __ggml_vocab_test__
__ggml_vocab_test__
Hello, y'all! How are you 😁 ?我想在apple工作1314151天～
__ggml_vocab_test__
!!!!!!
__ggml_vocab_test__
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222 299
44 34719
8302 49 553 44 483 38 4998 904 863 18445 247 1037 4995 13379 2924 9515 17823 54 56 54 57 54 58 54 11904 47892
9221 3226
56
56 56
56 56 56
@ -40,4 +41,6 @@
56 56 56 56 56 56 56
56 56 56 56 56 56 56 56
56 56 56 56 56 56 56 56 56
72 34269 102 33245 17234 121
1236 12266
353 736 8886 221 10883 4238 16101 28540 222 3822 272 246 327 4434 46 18445 152 46030 45022 142 13878 327 12585 19884 33773 40920 751 46 41839 5954 137 271 3822 137 271 244 56 244 56 56 244 56 56 56 244 56 56 56 56 244 56 56 56 56 56 244 56 56 56 56 56 56 244 56 56 56 56 56 56 56 244 56 56 56 56 56 56 56 56 244 56 51 56 244 56 516 56 244 56 1198 56 244 14566 246 14566 152 14566 265 30428 257 14566 261 30428 248 14566 268 14566 153 14566 277 30428 247 14566 277 14566 133 14566 152 14566 251 36570 247 1037 4995 13379 2924 9515 17823 54 56 54 57 54 58 54 11904 47892 20895 16625 13047 8389 1059 9504 40216 13858 2073 8983 12571 1539 10721 5918 9643 13298 932 31723 31330 9221 3226 35426 10400 457 4783 2602 349 121 1477 957 1200 2038 49 349 632 863 3673 68 349 82 666 3673 457 4650 1949 580 49 349 73 863 2144 1649 35941 68 2726 44 7728 331 44 113 81

1197
poetry.lock generated Normal file
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44
pyproject.toml Normal file
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@ -0,0 +1,44 @@
[tool.poetry]
name = "llama-cpp-scripts"
version = "0.0.0"
description = "Scripts that ship with llama.cpp"
authors = ["GGML <ggml@ggml.ai>"]
readme = "README.md"
homepage = "https://ggml.ai"
repository = "https://github.com/ggerganov/llama.cpp"
keywords = ["ggml", "gguf", "llama.cpp"]
packages = [{ include = "*.py", from = "." }]
classifiers = [
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
]
[tool.poetry.dependencies]
python = ">=3.9"
numpy = "^1.25.0"
sentencepiece = ">=0.1.98,<0.2.0"
transformers = ">=4.35.2,<5.0.0"
protobuf = ">=4.21.0,<5.0.0"
gguf = { path = "./gguf-py" }
torch = { version = "^2.2.0", source = "pytorch" }
[tool.poetry.dev-dependencies]
pytest = "^5.2"
# Force wheel + cpu
# For discussion and context see https://github.com/python-poetry/poetry#6409
[[tool.poetry.source]]
name = "pytorch"
url = "https://download.pytorch.org/whl/cpu"
priority = "explicit"
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.poetry.scripts]
llama-convert-hf-to-gguf = "convert_hf_to_gguf:main"
llama-convert-llama-ggml-to-gguf = "convert_llama_ggml_to_gguf:main"
llama-ggml-vk-generate-shaders = "ggml_vk_generate_shaders:main"

6
requirements.txt
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@ -6,6 +6,6 @@
-r ./requirements/requirements-convert-legacy-llama.txt
-r ./requirements/requirements-convert-hf-to-gguf.txt
-r ./requirements/requirements-convert-hf-to-gguf-update.txt
-r ./requirements/requirements-convert-llama-ggml-to-gguf.txt
-r ./requirements/requirements-convert_hf_to_gguf.txt
-r ./requirements/requirements-convert_hf_to_gguf_update.txt
-r ./requirements/requirements-convert_llama_ggml_to_gguf.txt

0
requirements/requirements-convert-hf-to-gguf-update.txt → requirements/requirements-convert_hf_to_gguf.txt
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0
requirements/requirements-convert-hf-to-gguf.txt → requirements/requirements-convert_hf_to_gguf_update.txt
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0
requirements/requirements-convert-llama-ggml-to-gguf.txt → requirements/requirements-convert_llama_ggml_to_gguf.txt
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4
scripts/check-requirements.sh
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@ -167,11 +167,11 @@ if (( do_cleanup )); then
fi
check_convert_script examples/convert-legacy-llama.py
for py in convert-*.py; do
for py in convert_*.py; do
# skip convert-hf-to-gguf-update.py
# TODO: the check is failing for some reason:
# https://github.com/ggerganov/llama.cpp/actions/runs/8875330981/job/24364557177?pr=6920
[[ $py == convert-hf-to-gguf-update.py ]] && continue
[[ $py == convert_hf_to_gguf_update.py ]] && continue
check_convert_script "$py"
done

1
scripts/pod-llama.sh
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@ -210,4 +210,3 @@ fi
# more benches
#GGML_CUDA=1 make -j && ./llama-batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1
#GGML_CUDA=1 make -j && ./llama-batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1

2
src/CMakeLists.txt
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@ -1,7 +1,5 @@
# TODO: should not use this
if (WIN32)
add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
if (BUILD_SHARED_LIBS)
set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
endif()

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src/llama.cpp
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1
src/unicode-data.cpp
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@ -7030,4 +7030,3 @@ const std::vector<range_nfd> unicode_ranges_nfd = { // start, last, nfd
{0x02FA1C, 0x02FA1C, 0x009F3B},
{0x02FA1D, 0x02FA1D, 0x02A600},
};

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