vbatts/llama.cpp
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Merge branch 'master' into concedo_experimental

Browse source 
# Conflicts:
#	Makefile
#	README.md
#	docs/token_generation_performance_tips.md
#	grammars/README.md
#	scripts/sync-ggml.sh
#	tests/CMakeLists.txt
#	tests/test-grad0.cpp
#	tests/test-opt.cpp
This commit is contained in:
Concedo 2023-11-15 16:59:53 +08:00
commit 35a97e14b2
49 changed files with 4972 additions and 3131 deletions
Download patch file Download diff file Expand all files Collapse all files

9
CMakeLists.txt
View file

@ -248,6 +248,15 @@ if (LLAMA_LTO)
endif()
endif()
# this version of Apple ld64 is buggy
execute_process(
COMMAND ${CMAKE_C_COMPILER} ${CMAKE_EXE_LINKER_FLAGS} -Wl,-v
ERROR_VARIABLE output
)
if (output MATCHES "dyld-1015\.7")
add_compile_definitions(HAVE_BUGGY_APPLE_LINKER)
endif()
# Architecture specific
# TODO: probably these flags need to be tweaked on some architectures
# feel free to update the Makefile for your architecture and send a pull request or issue

1
common/train.cpp
View file

@ -32,6 +32,7 @@ struct train_state * init_train_state() {
state->opt = new struct ggml_opt_context;
state->opt->ctx = NULL;
state->opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
state->opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;
state->opt->loss_after = 0.0f;
return state;

2
common/train.h
View file

@ -9,6 +9,8 @@
#include "ggml.h"
#include "llama.h"
#define LLAMA_TRAIN_MAX_NODES 16384
typedef std::string mt19937_state;
struct train_state {

2
convert-baichuan-hf-to-gguf.py
View file

@ -16,7 +16,7 @@ import torch
from sentencepiece import SentencePieceProcessor # type: ignore[import]
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf

30
convert-hf-to-gguf.py
View file

@ -150,8 +150,6 @@ class Model:
@staticmethod
def from_model_architecture(model_architecture):
if model_architecture == "StableLMEpochForCausalLM":
return StableLMModel
if model_architecture == "GPTNeoXForCausalLM":
return GPTNeoXModel
if model_architecture == "BloomForCausalLM":
@ -168,6 +166,8 @@ class Model:
return RefactModel
if model_architecture == "PersimmonForCausalLM":
return PersimmonModel
if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
return StableLMModel
return Model
def _is_model_safetensors(self) -> bool:
@ -201,6 +201,8 @@ class Model:
return gguf.MODEL_ARCH.REFACT
if arch == "PersimmonForCausalLM":
return gguf.MODEL_ARCH.PERSIMMON
if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
return gguf.MODEL_ARCH.STABLELM
raise NotImplementedError(f'Architecture "{arch}" not supported!')
@ -294,15 +296,6 @@ class Model:
special_vocab.add_to_gguf(self.gguf_writer)
class StableLMModel(Model):
def set_gguf_parameters(self):
super().set_gguf_parameters()
self.gguf_writer.add_rope_dimension_count(
int(self.hparams["rope_pct"] * (self.hparams["hidden_size"] // self.hparams["num_attention_heads"])),
)
self.gguf_writer.add_layer_norm_eps(1e-5)
class GPTNeoXModel(Model):
def set_gguf_parameters(self):
block_count = self.hparams["num_hidden_layers"]
@ -824,6 +817,21 @@ class PersimmonModel(Model):
self.gguf_writer.add_tensor(new_name, data)
class StableLMModel(Model):
def set_gguf_parameters(self):
hparams = self.hparams
block_count = hparams["num_hidden_layers"]
self.gguf_writer.add_name(dir_model.name)
self.gguf_writer.add_context_length(hparams["max_position_embeddings"])
self.gguf_writer.add_embedding_length(hparams["hidden_size"])
self.gguf_writer.add_block_count(block_count)
self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
self.gguf_writer.add_rope_dimension_count(int(hparams["rope_pct"]*(hparams["hidden_size"] // hparams["num_attention_heads"])))
self.gguf_writer.add_head_count(hparams["num_attention_heads"])
self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
self.gguf_writer.add_layer_norm_eps(1e-5)
###### CONVERSION LOGIC ######
def parse_args() -> argparse.Namespace:

24
convert-llama-ggml-to-gguf.py
View file

@ -12,29 +12,9 @@ import numpy as np
import os
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
# Note: Does not support GGML_QKK_64
QK_K = 256
# Items here are (block size, type size)
GGML_QUANT_SIZES = {
gguf.GGMLQuantizationType.F32 : (1, 4),
gguf.GGMLQuantizationType.F16 : (1, 2),
gguf.GGMLQuantizationType.Q4_0 : (32, 2 + 16),
gguf.GGMLQuantizationType.Q4_1 : (32, 2 + 2 + 16),
gguf.GGMLQuantizationType.Q5_0 : (32, 2 + 4 + 16),
gguf.GGMLQuantizationType.Q5_1 : (32, 2 + 2 + 4 + 16),
gguf.GGMLQuantizationType.Q8_0 : (32, 2 + 32),
gguf.GGMLQuantizationType.Q8_1 : (32, 4 + 4 + 32),
gguf.GGMLQuantizationType.Q2_K : (256, 2 + 2 + QK_K // 16 + QK_K // 4),
gguf.GGMLQuantizationType.Q3_K : (256, 2 + QK_K // 4 + QK_K // 8 + 12),
gguf.GGMLQuantizationType.Q4_K : (256, 2 + 2 + QK_K // 2 + 12),
gguf.GGMLQuantizationType.Q5_K : (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
gguf.GGMLQuantizationType.Q6_K : (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
gguf.GGMLQuantizationType.Q8_K : (256, 4 + QK_K + QK_K // 8),
}
class GGMLFormat(IntEnum):
GGML = 0
GGMF = 1
@ -125,7 +105,7 @@ class Tensor:
(n_dims, name_len, dtype) = struct.unpack('<3I', data[offset:offset + 12])
assert n_dims >= 0 and n_dims <= 4, f'Invalid tensor dimensions {n_dims}'
assert name_len < 4096, 'Absurd tensor name length'
quant = GGML_QUANT_SIZES.get(dtype)
quant = gguf.GGML_QUANT_SIZES.get(dtype)
assert quant is not None, 'Unknown tensor type'
(blksize, tysize) = quant
offset += 12

2
convert-persimmon-to-gguf.py
View file

@ -6,7 +6,7 @@ import argparse
from pathlib import Path
from sentencepiece import SentencePieceProcessor
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
def _flatten_dict(dct, tensors, prefix=None):

21
convert.py
View file

@ -3,11 +3,9 @@ from __future__ import annotations
import argparse
import concurrent.futures
import copy
import enum
import faulthandler
import functools
import io
import itertools
import json
import math
@ -23,14 +21,14 @@ from abc import ABCMeta, abstractmethod
from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor
from dataclasses import dataclass
from pathlib import Path
from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar
from typing import IO, TYPE_CHECKING, Any, Callable, Iterable, Literal, TypeVar
import numpy as np
from sentencepiece import SentencePieceProcessor
import os
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
if TYPE_CHECKING:
@ -851,7 +849,7 @@ class OutputFile:
elif isinstance(vocab, BpeVocab):
self.gguf.add_tokenizer_model("gpt2")
else:
raise ValueError(f'Unknown vocab type: Not BpeVocab or SentencePieceVocab')
raise ValueError('Unknown vocab type: Not BpeVocab or SentencePieceVocab')
self.gguf.add_token_list(tokens)
self.gguf.add_token_scores(scores)
self.gguf.add_token_types(toktypes)
@ -905,7 +903,7 @@ class OutputFile:
return dt.quantize(arr)
@staticmethod
def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess=gguf.GGUFEndian.LITTLE) -> None:
def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY, endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE) -> None:
check_vocab_size(params, vocab)
of = OutputFile(fname_out, endianess=endianess)
@ -1038,7 +1036,8 @@ def load_some_model(path: Path) -> ModelPlus:
# Be extra-friendly and accept either a file or a directory:
if path.is_dir():
# Check if it's a set of safetensors files first
files = list(path.glob("model-00001-of-*.safetensors"))
globs = ["model-00001-of-*.safetensors", "model.safetensors"]
files = [file for glob in globs for file in path.glob(glob)]
if not files:
# Try the PyTorch patterns too, with lower priority
globs = ["consolidated.00.pth", "pytorch_model-00001-of-*.bin", "*.pt", "pytorch_model.bin"]
@ -1114,14 +1113,18 @@ def do_dump_model(model_plus: ModelPlus) -> None:
def main(args_in: list[str] | None = None) -> None:
output_choices = ["f32", "f16"]
if np.uint32(1) == np.uint32(1).newbyteorder("<"):
# We currently only support Q8_0 output on little endian systems.
output_choices.append("q8_0")
parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file")
parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model")
parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file")
parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab")
parser.add_argument("--outtype", choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
parser.add_argument("--outtype", choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin, *.safetensors)")
parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
parser.add_argument("--ctx", type=int, help="model training context (default: based on input)")
parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY)

21
examples/benchmark/benchmark-matmult.cpp
View file

@ -172,7 +172,8 @@ int main(int argc, char ** argv) {
struct ggml_tensor * m11xm2 = ggml_mul_mat(ctx, m11, m2);
// printf("Creating compute graph\n");
struct ggml_cgraph gf = ggml_build_forward(m11xm2);
struct ggml_cgraph * gf = ggml_new_graph(ctx);
ggml_build_forward_expand(gf, m11xm2);
printf("n_threads=%i\n", benchmark_params.n_threads);
@ -181,9 +182,9 @@ int main(int argc, char ** argv) {
std::vector<uint8_t> work_buffer;
ggml_graph_compute_helper(work_buffer, &gf, benchmark_params.n_threads);
ggml_graph_compute_helper(work_buffer, gf, benchmark_params.n_threads);
TENSOR_DUMP(gf.nodes[0]);
TENSOR_DUMP(gf->nodes[0]);
printf("\n------ Test 2 - Matrix Mult via %s code\n", ggml_type_name(qtype));
@ -201,7 +202,8 @@ int main(int argc, char ** argv) {
struct ggml_tensor * q31 = ggml_mul_mat(ctx, q11, m2);
// printf("Creating compute graph\n");
struct ggml_cgraph gf31 = ggml_build_forward(q31);
struct ggml_cgraph * gf31 = ggml_new_graph(ctx);
ggml_build_forward_expand(gf31, q31);
// Set up a second graph computation to make sure we override the CPU cache lines
// printf("Creating new tensor q12 & Running quantize\n");
@ -212,7 +214,8 @@ int main(int argc, char ** argv) {
struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2);
//printf("Creating compute graph\n");
struct ggml_cgraph gf32 = ggml_build_forward(q32);
struct ggml_cgraph * gf32 = ggml_new_graph(ctx);
ggml_build_forward_expand(gf32, q32);
printf("n_threads=%i\n", benchmark_params.n_threads);
const int dimx = sizex;
@ -224,7 +227,7 @@ int main(int argc, char ** argv) {
// Let's use the F32 result from above as a reference for the quantized multiplication
float sum_of_F32_reference = tensor_sum_elements(gf.nodes[0]);
float sum_of_F32_reference = tensor_sum_elements(gf->nodes[0]);
printf("Iteration;NThreads; SizeX; SizeY; SizeZ; Required_FLOPS; Elapsed_u_Seconds; gigaFLOPS\n");
printf("=====================================================================================\n");
@ -234,7 +237,7 @@ int main(int argc, char ** argv) {
long long int start = ggml_time_us();
//printf("Running ggml_graph_compute\n");
ggml_graph_compute_helper(work_buffer, &gf31, benchmark_params.n_threads);
ggml_graph_compute_helper(work_buffer, gf31, benchmark_params.n_threads);
long long int stop = ggml_time_us();
long long int usec = stop-start;
@ -252,7 +255,7 @@ int main(int argc, char ** argv) {
// Check that the matrix multiplication result is in the right ballpark
// We cannot use the exact value from the F32 multiplication because the quantizuation will be slightly different
float sum_of_Q4_result = tensor_sum_elements(gf31.nodes[0]);
float sum_of_Q4_result = tensor_sum_elements(gf31->nodes[0]);
float delta = std::abs(sum_of_Q4_result - sum_of_F32_reference);
float allowed_delta = (sum_of_F32_reference) / 1000 / 1000; // Let's accept an epsilon of 10^-6
@ -267,7 +270,7 @@ int main(int argc, char ** argv) {
}
// Running a different graph computation to make sure we override the CPU cache lines
ggml_graph_compute_helper(work_buffer, &gf32, benchmark_params.n_threads);
ggml_graph_compute_helper(work_buffer, gf32, benchmark_params.n_threads);
}
printf("\n");
printf("Average%78.2f\n",gflops_sum/((double)benchmark_params.n_iterations));

4
examples/export-lora/export-lora.cpp
View file

@ -240,7 +240,7 @@ static struct lora_data * load_lora(struct lora_info * info) {
}
struct ggml_init_params params_ggml;
params_ggml.mem_size = ggml_tensor_overhead() * GGML_MAX_NODES;
params_ggml.mem_size = ggml_tensor_overhead() * GGML_DEFAULT_GRAPH_SIZE;
params_ggml.mem_buffer = NULL;
params_ggml.no_alloc = true;
result->ctx = ggml_init(params_ggml);
@ -334,7 +334,7 @@ static bool apply_lora(struct ggml_tensor * tensor, struct lora_data * lora, int
float scaling = lora->info.scale * (float)lora->lora_alpha / (float)lora->lora_r;
struct ggml_init_params params;
params.mem_size = GGML_OBJECT_SIZE + GGML_GRAPH_SIZE + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5;
params.mem_size = GGML_OBJECT_SIZE + ggml_graph_overhead() + ggml_tensor_overhead()*4 + GGML_MEM_ALIGN*5;
params.mem_buffer = NULL;
params.no_alloc = true;
struct ggml_context * ctx = NULL;

23
examples/finetune/finetune.cpp
View file

@ -772,7 +772,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs(
if (enable_checkpointing) {
ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size());
} else {
*gb = *gf;
ggml_graph_cpy(gf, gb);
ggml_build_backward_expand(ctx, gf, gb, true);
}
@ -1615,6 +1615,7 @@ int main(int argc, char ** argv) {
opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
opt->params.print_forward_graph = false;
opt->params.print_backward_graph = false;
opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;
opt->params.n_threads = params.common.n_threads;
opt->params.past = params.common.opt_past;
opt->params.delta = params.common.opt_delta;
@ -1741,11 +1742,9 @@ int main(int argc, char ** argv) {
ggml_allocr_free(alloc);
// context for compute tensors without their data
size_t estimated_compute_size_wo_data = (
ggml_tensor_overhead()*GGML_MAX_NODES*2
+ (GGML_OBJECT_SIZE+GGML_GRAPH_SIZE)*(
params.common.use_checkpointing ? 3 : 2
)
const size_t estimated_compute_size_wo_data = (
2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() +
(params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true))
);
struct ggml_init_params ctx_compute_params = {
estimated_compute_size_wo_data, // mem_size
@ -1768,11 +1767,11 @@ int main(int argc, char ** argv) {
for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
ctx_compute = ggml_init(ctx_compute_params);
alloc = ggml_allocr_new_measure(tensor_alignment);
gf = ggml_new_graph(ctx_compute);
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = (enum ggml_cgraph_eval_order) order;
gb = ggml_new_graph(ctx_compute);
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gb_tmp = params.common.use_checkpointing
? ggml_new_graph(ctx_compute)
? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
: NULL;
loss = llama_build_lora_finetune_graphs(
&model, &lora, alloc, ctx_compute,
@ -1801,11 +1800,11 @@ int main(int argc, char ** argv) {
mem_compute_data.resize(max_compute_size);
ctx_compute = ggml_init(ctx_compute_params);
alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
gf = ggml_new_graph(ctx_compute);
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = best_order;
gb = ggml_new_graph(ctx_compute);
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gb_tmp = params.common.use_checkpointing
? ggml_new_graph(ctx_compute)
? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
: NULL;
loss = llama_build_lora_finetune_graphs(
&model, &lora, alloc, ctx_compute,

4
examples/llava/clip.cpp
View file

@ -664,7 +664,7 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
// measure mem requirement and allocate
{
static const size_t tensor_alignment = 32;
new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
new_clip->buf_compute.resize(ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead());
new_clip->alloc = ggml_allocr_new_measure(tensor_alignment);
clip_image_f32_batch batch;
batch.size = 1;
@ -761,7 +761,7 @@ bool clip_image_preprocess(const clip_ctx * ctx, const clip_image_u8 * img, clip
temp->ny = img->ny;
temp->size = img->size;
temp->data = new uint8_t[temp->size]();
*temp->data = *img->data; // copy
memcpy(&temp->data[0], &img->data[0], temp->size); // copy
}
const int nx = temp->nx;

2
examples/main/README.md
View file

@ -142,7 +142,7 @@ The `--ctx-size` option allows you to set the size of the prompt context used by
### Extended Context Size
Some fine-tuned models have extened the context length by scaling RoPE. For example, if the original pretrained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8.
Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8.
- `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model.

10
examples/metal/metal.cpp
View file

@ -34,7 +34,7 @@ int main(int argc, char ** argv) {
struct ggml_context * ctx_data = NULL;
struct ggml_context * ctx_eval = NULL;
struct ggml_cgraph gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval);
struct ggml_cgraph * gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval);
// this allocates all Metal resources and memory buffers
auto * ctx_metal = ggml_metal_init(1);
@ -46,13 +46,13 @@ int main(int argc, char ** argv) {
// main
{
struct ggml_tensor * input = ggml_graph_get_tensor(&gf, "embd");
struct ggml_tensor * input = ggml_graph_get_tensor(gf, "embd");
*(int32_t *) input->data = 1; // BOS
ggml_metal_set_tensor(ctx_metal, input);
// warmup
ggml_metal_graph_compute(ctx_metal, &gf);
ggml_metal_graph_compute(ctx_metal, gf);
const int n_iter = 16;
@ -60,7 +60,7 @@ int main(int argc, char ** argv) {
// the actual inference happens here
for (int i = 0; i < n_iter; ++i) {
ggml_metal_graph_compute(ctx_metal, &gf);
ggml_metal_graph_compute(ctx_metal, gf);
}
const int64_t t1 = ggml_time_us();
@ -70,7 +70,7 @@ int main(int argc, char ** argv) {
// debug output
{
struct ggml_tensor * logits = gf.nodes[gf.n_nodes - 1];
struct ggml_tensor * logits = gf->nodes[gf->n_nodes - 1];
ggml_metal_get_tensor(ctx_metal, logits);
float * ptr = (float *) ggml_get_data(logits);

2
examples/parallel/README.md
View file

@ -1,3 +1,3 @@
# llama.cpp/example/parallel
Simplified simluation for serving incoming requests in parallel
Simplified simulation of serving incoming requests in parallel

18
examples/server/server.cpp
View file

@ -1558,15 +1558,6 @@ struct llama_server_context
slot.num_prompt_tokens = prompt_tokens.size();
if (!slot.params.cache_prompt)
{
llama_sampling_reset(slot.ctx_sampling);
slot.n_past = 0;
slot.num_prompt_tokens_processed = slot.num_prompt_tokens;
}
else
{
if (slot.params.n_keep < 0)
{
slot.params.n_keep = slot.num_prompt_tokens;
@ -1596,6 +1587,15 @@ struct llama_server_context
GGML_ASSERT(slot.num_prompt_tokens < slot.n_ctx);
}
if (!slot.params.cache_prompt)
{
llama_sampling_reset(slot.ctx_sampling);
slot.n_past = 0;
slot.num_prompt_tokens_processed = slot.num_prompt_tokens;
}
else
{
// push the prompt into the sampling context (do not apply grammar)
for (auto &token : prompt_tokens)
{

2
examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py
View file

@ -9,7 +9,7 @@ import numpy as np
from pathlib import Path
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py' / 'gguf'))
sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py'))
import gguf
# gguf constants

23
examples/train-text-from-scratch/train-text-from-scratch.cpp
View file

@ -436,7 +436,7 @@ static struct ggml_tensor * llama_build_train_graphs(
if (enable_checkpointing) {
ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size());
} else {
*gb = *gf;
ggml_graph_cpy(gf, gb);
ggml_build_backward_expand(ctx, gf, gb, true);
}
@ -1006,6 +1006,7 @@ int main(int argc, char ** argv) {
opt->params = ggml_opt_default_params(GGML_OPT_ADAM);
opt->params.print_forward_graph = false;
opt->params.print_backward_graph = false;
opt->params.graph_size = LLAMA_TRAIN_MAX_NODES;
opt->params.n_threads = params.common.n_threads;
opt->params.past = params.common.opt_past;
opt->params.delta = params.common.opt_delta;
@ -1108,11 +1109,9 @@ int main(int argc, char ** argv) {
ggml_allocr_free(alloc);
// context for compute tensors without their data
size_t estimated_compute_size_wo_data = (
ggml_tensor_overhead()*GGML_MAX_NODES*2
+ (GGML_OBJECT_SIZE+GGML_GRAPH_SIZE)*(
params.common.use_checkpointing ? 3 : 2
)
const size_t estimated_compute_size_wo_data = (
2*LLAMA_TRAIN_MAX_NODES*ggml_tensor_overhead() +
(params.common.use_checkpointing ? 3 : 2)*(GGML_OBJECT_SIZE+ggml_graph_overhead_custom(LLAMA_TRAIN_MAX_NODES, true))
);
struct ggml_init_params ctx_compute_params = {
estimated_compute_size_wo_data, // mem_size
@ -1135,11 +1134,11 @@ int main(int argc, char ** argv) {
for (unsigned order = 0; order < (unsigned) GGML_CGRAPH_EVAL_ORDER_COUNT; ++order) {
ctx_compute = ggml_init(ctx_compute_params);
alloc = ggml_allocr_new_measure(tensor_alignment);
gf = ggml_new_graph(ctx_compute);
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = (enum ggml_cgraph_eval_order) order;
gb = ggml_new_graph(ctx_compute);
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gb_tmp = params.common.use_checkpointing
? ggml_new_graph(ctx_compute)
? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
: NULL;
loss = llama_build_train_graphs(
&model, alloc, ctx_compute,
@ -1168,11 +1167,11 @@ int main(int argc, char ** argv) {
mem_compute_data.resize(max_compute_size);
ctx_compute = ggml_init(ctx_compute_params);
alloc = ggml_allocr_new(mem_compute_data.data(), mem_compute_data.size(), tensor_alignment);
gf = ggml_new_graph(ctx_compute);
gf = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gf->order = best_order;
gb = ggml_new_graph(ctx_compute);
gb = ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true);
gb_tmp = params.common.use_checkpointing
? ggml_new_graph(ctx_compute)
? ggml_new_graph_custom(ctx_compute, LLAMA_TRAIN_MAX_NODES, true)
: NULL;
loss = llama_build_train_graphs(
&model, alloc, ctx_compute,

472
ggml-alloc.c
View file

@ -1,51 +1,21 @@
#include "ggml-alloc.h"
#include "ggml-backend.h"
#include "ggml-backend-impl.h"
#include "ggml.h"
#include "ggml-impl.h"
#include <assert.h>
#include <limits.h>
#include <stdarg.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#define UNUSED(x) (void)(x)
#define MAX(a, b) ((a) > (b) ? (a) : (b))
#define GGML_MAX_CONCUR (2*GGML_MAX_NODES)
#define MAX_FREE_BLOCKS 256
//#define GGML_ALLOCATOR_DEBUG
//#define AT_PRINTF printf
#define AT_PRINTF(...) ((void)0)
struct hash_node {
struct ggml_tensor * t;
int n_children;
int n_views;
};
static size_t hash(void * p) {
return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE;
}
static struct hash_node * hash_get(struct hash_node hash_table[], struct ggml_tensor * t) {
size_t h = hash(t);
// linear probing
size_t i = h;
while (hash_table[i].t != NULL) {
if (hash_table[i].t == t) {
return &hash_table[i];
}
i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE;
if (i == h) {
// hash table is full
GGML_ASSERT(false);
}
}
hash_table[i].t = t;
return &hash_table[i];
}
//#define AT_PRINTF(...) fprintf(stderr, __VA_ARGS__)
#define AT_PRINTF(...)
// TODO: GGML_PAD ?
static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
@ -59,20 +29,18 @@ struct free_block {
size_t size;
};
#define MAX_FREE_BLOCKS 256
struct ggml_allocr {
struct ggml_tallocr {
struct ggml_backend_buffer * buffer;
bool buffer_owned;
void * data;
void * base;
size_t alignment;
int n_free_blocks;
struct free_block free_blocks[MAX_FREE_BLOCKS];
struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE];
size_t max_size;
bool measure;
int parse_seq[GGML_MAX_CONCUR];
int parse_seq_len;
#ifdef GGML_ALLOCATOR_DEBUG
struct ggml_tensor * allocated_tensors[1024];
@ -80,7 +48,7 @@ struct ggml_allocr {
};
#ifdef GGML_ALLOCATOR_DEBUG
static void add_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
static void add_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i] == NULL) {
alloc->allocated_tensors[i] = tensor;
@ -89,7 +57,7 @@ static void add_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor
}
GGML_ASSERT(!"out of allocated_tensors");
}
static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
static void remove_allocated_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i] == tensor ||
(alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) {
@ -103,7 +71,7 @@ static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tens
#endif
// check if a tensor is allocated by this buffer
static bool ggml_allocr_is_own(struct ggml_allocr * alloc, const struct ggml_tensor * tensor) {
static bool ggml_tallocr_is_own(ggml_tallocr_t alloc, const struct ggml_tensor * tensor) {
return tensor->buffer == alloc->buffer;
}
@ -111,7 +79,7 @@ static bool ggml_is_view(struct ggml_tensor * t) {
return t->view_src != NULL;
}
void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
GGML_ASSERT(!ggml_is_view(tensor)); // views generally get data pointer from one of their sources
GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated
@ -162,9 +130,10 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor)
}
tensor->data = addr;
AT_PRINTF("%s: allocated data at %p\n", __func__, tensor->data);
tensor->buffer = alloc->buffer;
if (!alloc->measure) {
ggml_backend_buffer_init_tensor(alloc->buffer, tensor);
}
#ifdef GGML_ALLOCATOR_DEBUG
add_allocated_tensor(alloc, tensor);
@ -180,16 +149,16 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor)
}
#endif
alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->data + size);
alloc->max_size = MAX(alloc->max_size, (char*)addr - (char*)alloc->base + size);
}
// this is a very naive implementation, but for our case the number of free blocks should be very small
static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
if (ggml_allocr_is_own(alloc, tensor) == false) {
static void ggml_tallocr_free_tensor(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
if (ggml_tallocr_is_own(alloc, tensor) == false) {
// the tensor was not allocated in this buffer
// this can happen because the graph allocator will try to free weights and other tensors from different buffers
// the easiest way to deal with this is just to ignore it
AT_PRINTF("ignoring %s (their buffer: %p, our buffer: %p)\n", tensor->name, (void *)tensor->buffer, (void *)alloc->buffer);
// AT_PRINTF("ignoring %s (their buffer: %p, our buffer: %p)\n", tensor->name, (void *)tensor->buffer, (void *)alloc->buffer);
return;
}
@ -199,7 +168,9 @@ static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tens
size = aligned_offset(NULL, size, alloc->alignment);
AT_PRINTF("%s: freeing %s at %p (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, ptr, size, alloc->n_free_blocks);
if (!alloc->measure) {
ggml_backend_buffer_free_tensor(alloc->buffer, tensor);
}
#ifdef GGML_ALLOCATOR_DEBUG
remove_allocated_tensor(alloc, tensor);
@ -253,91 +224,180 @@ static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tens
alloc->n_free_blocks++;
}
void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n) {
for (int i = 0; i < n; i++) {
alloc->parse_seq[i] = list[i];
}
alloc->parse_seq_len = n;
}
void ggml_allocr_reset(struct ggml_allocr * alloc) {
void ggml_tallocr_reset(ggml_tallocr_t alloc) {
alloc->n_free_blocks = 1;
size_t align_offset = aligned_offset(alloc->data, 0, alloc->alignment);
alloc->free_blocks[0].addr = (char *)alloc->data + align_offset;
size_t align_offset = aligned_offset(alloc->base, 0, alloc->alignment);
alloc->free_blocks[0].addr = (char *)alloc->base + align_offset;
if (alloc->measure) {
alloc->free_blocks[0].size = SIZE_MAX/2; // restrict maximum size of a measure allocator to half size_t max to avoid overflows
} else {
alloc->free_blocks[0].size = ggml_backend_buffer_get_size(alloc->buffer) - align_offset;
}
}
struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) {
ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment) {
struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(NULL, data, size);
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr));
ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr));
*alloc = (struct ggml_allocr){
*alloc = (struct ggml_tallocr) {
/*.buffer = */ buffer,
/*.buffer_owned = */ true,
/*.base = */ ggml_backend_buffer_get_base(buffer),
/*.alignment = */ alignment,
/*.n_free_blocks = */ 0,
/*.free_blocks = */ {{0}},
/*.hash_table = */ {{0}},
/*.max_size = */ 0,
/*.measure = */ false,
/*.parse_seq = */ {0},
/*.parse_seq_len = */ 0,
#ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ {0},
#endif
};
ggml_allocr_reset(alloc);
ggml_tallocr_reset(alloc);
return alloc;
}
struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
struct ggml_allocr * alloc = ggml_allocr_new((void *)0x1000, (size_t)-0x1001, alignment);
ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment) {
ggml_tallocr_t alloc = ggml_tallocr_new((void *)0x1000, SIZE_MAX/2, alignment);
alloc->measure = true;
return alloc;
}
struct ggml_allocr * ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer) {
struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr));
ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend) {
// create a backend buffer to get the correct tensor allocation sizes
ggml_backend_buffer_t buffer = ggml_backend_alloc_buffer(backend, 1);
*alloc = (struct ggml_allocr){
// TODO: move alloc initialization to a common ggml_tallocr_new_impl function
ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer);
alloc->buffer_owned = true;
alloc->measure = true;
ggml_tallocr_reset(alloc);
return alloc;
}
ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size) {
ggml_backend_buffer_t buffer = ggml_backend_alloc_buffer(backend, size);
ggml_tallocr_t alloc = ggml_tallocr_new_from_buffer(buffer);
alloc->buffer_owned = true;
return alloc;
}
ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer) {
ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr));
*alloc = (struct ggml_tallocr) {
/*.buffer = */ buffer,
/*.buffer_owned = */ false,
/*.base = */ ggml_backend_buffer_get_base(buffer),
/*.alignment = */ ggml_backend_buffer_get_alignment(buffer),
/*.n_free_blocks = */ 0,
/*.free_blocks = */ {{0}},
/*.hash_table = */ {{0}},
/*.max_size = */ 0,
/*.measure = */ false,
/*.parse_seq = */ {0},
/*.parse_seq_len = */ 0,
#ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ {0},
#endif
};
ggml_allocr_reset(alloc);
ggml_tallocr_reset(alloc);
return alloc;
}
void ggml_allocr_free(struct ggml_allocr * alloc) {
struct ggml_backend_buffer * ggml_tallocr_get_buffer(ggml_tallocr_t alloc) {
return alloc->buffer;
}
void ggml_tallocr_free(ggml_tallocr_t alloc) {
if (alloc == NULL) {
return;
}
if (alloc->buffer_owned) {
ggml_backend_buffer_free(alloc->buffer);
}
free(alloc);
}
bool ggml_allocr_is_measure(struct ggml_allocr * alloc) {
bool ggml_tallocr_is_measure(ggml_tallocr_t alloc) {
return alloc->measure;
}
//////////// compute graph allocator
size_t ggml_tallocr_max_size(ggml_tallocr_t alloc) {
return alloc->max_size;
}
// graph allocator
struct hash_node {
int n_children;
int n_views;
};
struct ggml_gallocr {
ggml_tallocr_t talloc;
struct ggml_hash_set hash_set;
struct hash_node * hash_values;
size_t hash_values_size;
ggml_tallocr_t * hash_allocs;
int * parse_seq;
int parse_seq_len;
};
ggml_gallocr_t ggml_gallocr_new(void) {
ggml_gallocr_t galloc = (ggml_gallocr_t)malloc(sizeof(struct ggml_gallocr));
*galloc = (struct ggml_gallocr) {
/*.talloc = */ NULL,
/*.hash_set = */ {0},
/*.hash_values = */ NULL,
/*.hash_values_size = */ 0,
/*.hash_allocs = */ NULL,
/*.parse_seq = */ NULL,
/*.parse_seq_len = */ 0,
};
return galloc;
}
void ggml_gallocr_free(ggml_gallocr_t galloc) {
if (galloc == NULL) {
return;
}
if (galloc->hash_set.keys != NULL) {
free(galloc->hash_set.keys);
}
if (galloc->hash_values != NULL) {
free(galloc->hash_values);
}
if (galloc->hash_allocs != NULL) {
free(galloc->hash_allocs);
}
if (galloc->parse_seq != NULL) {
free(galloc->parse_seq);
}
free(galloc);
}
void ggml_gallocr_set_parse_seq(ggml_gallocr_t galloc, const int * list, int n) {
free(galloc->parse_seq);
galloc->parse_seq = malloc(sizeof(int) * n);
for (int i = 0; i < n; i++) {
galloc->parse_seq[i] = list[i];
}
galloc->parse_seq_len = n;
}
static struct hash_node * hash_get(ggml_gallocr_t galloc, struct ggml_tensor * t) {
size_t i = ggml_hash_find_or_insert(galloc->hash_set, t);
return &galloc->hash_values[i];
}
static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
if (a->type != b->type) {
@ -378,27 +438,40 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
}
}
static void init_view(struct ggml_allocr * alloc, struct ggml_tensor * view, bool update_backend) {
assert(view->view_src != NULL && view->view_src->data != NULL);
static ggml_tallocr_t node_tallocr(ggml_gallocr_t galloc, struct ggml_tensor * node) {
if (galloc->talloc != NULL) {
return galloc->talloc;
}
return galloc->hash_allocs[ggml_hash_find_or_insert(galloc->hash_set, node)];
}
static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool update_backend) {
ggml_tallocr_t alloc = node_tallocr(galloc, view);
//printf("init_view: %s from src %s\n", view->name, view->view_src->name);
GGML_ASSERT(view->view_src != NULL && view->view_src->data != NULL);
if (update_backend) {
view->backend = view->view_src->backend;
}
view->buffer = view->view_src->buffer;
view->data = (char *)view->view_src->data + view->view_offs;
// FIXME: the view should be initialized by the owning buffer, but currently this breaks the CUDA backend
// due to the ggml_tensor_extra_gpu ring buffer overwriting the KV cache extras
assert(ggml_allocr_is_measure(alloc) || !view->buffer || view->buffer->backend == alloc->buffer->backend);
assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->backend == alloc->buffer->backend);
if (!alloc->measure) {
ggml_backend_buffer_init_tensor(alloc->buffer, view);
}
}
static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) {
struct hash_node * ht = alloc->hash_table;
static void allocate_node(ggml_gallocr_t galloc, struct ggml_tensor * node) {
ggml_tallocr_t alloc = node_tallocr(galloc, node);
if (node->data == NULL) {
if (ggml_is_view(node)) {
init_view(alloc, node, true);
init_view(galloc, node, true);
} else {
// see if we can reuse a parent's buffer (inplace)
if (ggml_op_can_inplace(node->op)) {
@ -409,16 +482,16 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
}
// if the node's data is external, then we cannot re-use it
if (ggml_allocr_is_own(alloc, parent) == false) {
if (ggml_tallocr_is_own(alloc, parent) == false) {
AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data);
continue;
}
struct hash_node * p_hn = hash_get(ht, parent);
struct hash_node * p_hn = hash_get(galloc, parent);
if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
if (ggml_is_view(parent)) {
struct ggml_tensor * view_src = parent->view_src;
struct hash_node * view_src_hn = hash_get(ht, view_src);
struct hash_node * view_src_hn = hash_get(galloc, view_src);
if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
// TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
// the parent's data that it will need later (same layout requirement). the problem is that then
@ -428,45 +501,44 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
node->view_src = view_src;
view_src_hn->n_views += 1;
init_view(alloc, node, false);
init_view(galloc, node, false);
return;
}
} else {
AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
node->view_src = parent;
p_hn->n_views += 1;
init_view(alloc, node, false);
init_view(galloc, node, false);
return;
}
}
}
}
ggml_allocr_alloc(alloc, node);
ggml_tallocr_alloc(alloc, node);
}
}
}
size_t ggml_allocr_alloc_graph_n(
struct ggml_allocr * alloc,
struct ggml_cgraph ** graphs, int n_graphs,
struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) {
static void free_node(ggml_gallocr_t galloc, struct ggml_tensor * node) {
ggml_tallocr_t alloc = node_tallocr(galloc, node);
// reset hash table
struct hash_node * ht = alloc->hash_table;
memset(ht, 0, sizeof(struct hash_node) * GGML_GRAPH_HASHTABLE_SIZE);
ggml_tallocr_free_tensor(alloc, node);
}
static void ggml_tallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * gf) {
const int * parse_seq = galloc->parse_seq;
int parse_seq_len = galloc->parse_seq_len;
// count number of children and views
for (int g = 0; g < n_graphs; g++) {
struct ggml_cgraph * gf = graphs[g];
for (int i = 0; i < gf->n_nodes; i++) {
struct ggml_tensor * node = gf->nodes[i];
if (ggml_is_view(node)) {
struct ggml_tensor * view_src = node->view_src;
hash_get(ht, view_src)->n_views += 1;
hash_get(galloc, view_src)->n_views += 1;
if (node->buffer == NULL && node->data != NULL) {
// view of a pre-allocated tensor, didn't call init_view() yet
init_view(alloc, node, true);
init_view(galloc, node, true);
}
}
@ -475,34 +547,22 @@ size_t ggml_allocr_alloc_graph_n(
if (parent == NULL) {
break;
}
hash_get(ht, parent)->n_children += 1;
hash_get(galloc, parent)->n_children += 1;
if (ggml_is_view(parent) && parent->buffer == NULL && parent->data != NULL) {
init_view(alloc, parent, true);
}
init_view(galloc, parent, true);
}
}
}
// allocate tensors
for (int g = 0; g < n_graphs; g++) {
struct ggml_cgraph * gf = graphs[g];
AT_PRINTF("####### graph %d/%d\n", g, n_graphs);
// graph inputs are allocated first to ensure that they are not overwritten by each other
if (inputs != NULL && inputs[g] != NULL) {
for (int i = 0; inputs[g][i] != NULL; i++) {
struct ggml_tensor * input = inputs[g][i];
AT_PRINTF("input: %s\n", input->name);
allocate_node(alloc, input);
}
}
// if we have parse_seq then we allocate nodes following the list, and we only free nodes at barriers
int last_barrier_pos = 0;
int n_nodes = alloc->parse_seq_len ? alloc->parse_seq_len : gf->n_nodes;
int n_nodes = parse_seq_len ? parse_seq_len : gf->n_nodes;
for (int ind = 0; ind < n_nodes; ind++) {
// allocate a node if there is no parse_seq or this is not a barrier
if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] != -1) {
int i = alloc->parse_seq_len ? alloc->parse_seq[ind] : ind;
if (parse_seq_len == 0 || parse_seq[ind] != -1) {
int i = parse_seq_len ? parse_seq[ind] : ind;
struct ggml_tensor * node = gf->nodes[i];
// allocate parents (leafs)
@ -511,11 +571,11 @@ size_t ggml_allocr_alloc_graph_n(
if (parent == NULL) {
break;
}
allocate_node(alloc, parent);
allocate_node(galloc, parent);
}
// allocate node
allocate_node(alloc, node);
allocate_node(galloc, node);
AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
for (int j = 0; j < GGML_MAX_SRC; j++) {
@ -534,11 +594,11 @@ size_t ggml_allocr_alloc_graph_n(
// update parents
// update immediately if there is no parse_seq
// update only at barriers if there is parse_seq
if ((alloc->parse_seq_len == 0) || alloc->parse_seq[ind] == -1) {
int update_start = alloc->parse_seq_len ? last_barrier_pos : ind;
int update_end = alloc->parse_seq_len ? ind : ind + 1;
if ((parse_seq_len == 0) || parse_seq[ind] == -1) {
int update_start = parse_seq_len ? last_barrier_pos : ind;
int update_end = parse_seq_len ? ind : ind + 1;
for (int i = update_start; i < update_end; i++) {
int node_i = alloc->parse_seq_len ? alloc->parse_seq[i] : i;
int node_i = parse_seq_len ? parse_seq[i] : i;
struct ggml_tensor * node = gf->nodes[node_i];
for (int j = 0; j < GGML_MAX_SRC; j++) {
@ -546,7 +606,7 @@ size_t ggml_allocr_alloc_graph_n(
if (parent == NULL) {
break;
}
struct hash_node * p_hn = hash_get(ht, parent);
struct hash_node * p_hn = hash_get(galloc, parent);
p_hn->n_children -= 1;
//AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
@ -554,44 +614,154 @@ size_t ggml_allocr_alloc_graph_n(
if (p_hn->n_children == 0 && p_hn->n_views == 0) {
if (ggml_is_view(parent)) {
struct ggml_tensor * view_src = parent->view_src;
struct hash_node * view_src_hn = hash_get(ht, view_src);
struct hash_node * view_src_hn = hash_get(galloc, view_src);
view_src_hn->n_views -= 1;
AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views);
if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
ggml_allocr_free_tensor(alloc, view_src);
if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0) {
free_node(galloc, view_src);
}
}
else {
if (parent->data != node->data) {
ggml_allocr_free_tensor(alloc, parent);
}
free_node(galloc, parent);
}
}
}
}
AT_PRINTF("\n");
if (alloc->parse_seq_len) {
if (parse_seq_len) {
last_barrier_pos = ind + 1;
}
}
}
// free graph outputs here that wouldn't be freed otherwise because they have no children
if (outputs != NULL && outputs[g] != NULL) {
for (int i = 0; outputs[g][i] != NULL; i++) {
struct ggml_tensor * output = outputs[g][i];
AT_PRINTF("output: %s\n", output->name);
ggml_allocr_free_tensor(alloc, output);
}
}
}
return alloc->max_size;
}
size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) {
return ggml_allocr_alloc_graph_n(alloc, &graph, 1, NULL, NULL);
size_t ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, ggml_tallocr_t talloc, struct ggml_cgraph * graph) {
size_t hash_size = graph->visited_hash_table.size;
// check if the hash table is initialized and large enough
if (galloc->hash_set.size < hash_size) {
if (galloc->hash_set.keys != NULL) {
free(galloc->hash_set.keys);
}
if (galloc->hash_values != NULL) {
free(galloc->hash_values);
}
galloc->hash_set.keys = malloc(sizeof(struct ggml_tensor *) * hash_size);
galloc->hash_set.size = hash_size;
galloc->hash_values = malloc(sizeof(struct hash_node) * hash_size);
}
// reset hash table
memset(galloc->hash_set.keys, 0, sizeof(struct ggml_tensor *) * hash_size);
memset(galloc->hash_values, 0, sizeof(struct hash_node) * hash_size);
galloc->talloc = talloc;
ggml_tallocr_alloc_graph_impl(galloc, graph);
galloc->talloc = NULL;
size_t max_size = ggml_tallocr_max_size(talloc);
return max_size;
}
size_t ggml_allocr_max_size(struct ggml_allocr * alloc) {
return alloc->max_size;
void ggml_gallocr_alloc_graph_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, struct ggml_hash_set hash_set, ggml_tallocr_t * hash_node_talloc) {
const size_t hash_size = hash_set.size;
GGML_ASSERT(hash_size >= (size_t)(graph->n_nodes + graph->n_leafs));
galloc->talloc = NULL;
// alloc hash_values if needed
if (galloc->hash_values == NULL || galloc->hash_values_size < hash_size) {
free(galloc->hash_values);
galloc->hash_values = malloc(sizeof(struct hash_node) * hash_size);
galloc->hash_values_size = hash_size;
}
// free hash_set.keys if needed
if (galloc->hash_set.keys != NULL) {
free(galloc->hash_set.keys);
}
galloc->hash_set = hash_set;
// reset hash values
memset(galloc->hash_values, 0, sizeof(struct hash_node) * hash_size);
galloc->hash_allocs = hash_node_talloc;
ggml_tallocr_alloc_graph_impl(galloc, graph);
// remove unowned resources
galloc->hash_set.keys = NULL;
galloc->hash_allocs = NULL;
}
// legacy API wrapper
struct ggml_allocr {
ggml_tallocr_t talloc;
ggml_gallocr_t galloc;
};
static ggml_allocr_t ggml_allocr_new_impl(ggml_tallocr_t talloc) {
ggml_allocr_t alloc = (ggml_allocr_t)malloc(sizeof(struct ggml_allocr));
*alloc = (struct ggml_allocr) {
/*.talloc = */ talloc,
/*.galloc = */ ggml_gallocr_new(),
};
return alloc;
}
ggml_allocr_t ggml_allocr_new(void * data, size_t size, size_t alignment) {
return ggml_allocr_new_impl(ggml_tallocr_new(data, size, alignment));
}
ggml_allocr_t ggml_allocr_new_measure(size_t alignment) {
return ggml_allocr_new_impl(ggml_tallocr_new_measure(alignment));
}
ggml_allocr_t ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer) {
return ggml_allocr_new_impl(ggml_tallocr_new_from_buffer(buffer));
}
ggml_allocr_t ggml_allocr_new_from_backend(struct ggml_backend * backend, size_t size) {
return ggml_allocr_new_impl(ggml_tallocr_new_from_backend(backend, size));
}
ggml_allocr_t ggml_allocr_new_measure_from_backend(struct ggml_backend * backend) {
return ggml_allocr_new_impl(ggml_tallocr_new_measure_from_backend(backend));
}
struct ggml_backend_buffer * ggml_allocr_get_buffer(ggml_allocr_t alloc) {
return ggml_tallocr_get_buffer(alloc->talloc);
}
void ggml_allocr_set_parse_seq(ggml_allocr_t alloc, const int * list, int n) {
ggml_gallocr_set_parse_seq(alloc->galloc, list, n);
}
void ggml_allocr_free(ggml_allocr_t alloc) {
ggml_gallocr_free(alloc->galloc);
ggml_tallocr_free(alloc->talloc);
free(alloc);
}
bool ggml_allocr_is_measure(ggml_allocr_t alloc) {
return ggml_tallocr_is_measure(alloc->talloc);
}
void ggml_allocr_reset(ggml_allocr_t alloc) {
ggml_tallocr_reset(alloc->talloc);
}
void ggml_allocr_alloc(ggml_allocr_t alloc, struct ggml_tensor * tensor) {
ggml_tallocr_alloc(alloc->talloc, tensor);
}
size_t ggml_allocr_max_size(ggml_allocr_t alloc) {
return ggml_tallocr_max_size(alloc->talloc);
}
size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph) {
return ggml_gallocr_alloc_graph(alloc->galloc, alloc->talloc, graph);
}

80
ggml-alloc.h
View file

@ -6,27 +6,79 @@
extern "C" {
#endif
struct ggml_backend;
struct ggml_backend_buffer;
GGML_API struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment);
GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
GGML_API struct ggml_allocr * ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer);
//
// Legacy API
//
typedef struct ggml_allocr * ggml_allocr_t;
// initialize allocator for use with CPU backend only
GGML_API ggml_allocr_t ggml_allocr_new(void * data, size_t size, size_t alignment);
GGML_API ggml_allocr_t ggml_allocr_new_measure(size_t alignment);
// initialize allocator for use with ggml-backend
GGML_API ggml_allocr_t ggml_allocr_new_from_buffer(struct ggml_backend_buffer * buffer);
GGML_API ggml_allocr_t ggml_allocr_new_from_backend(struct ggml_backend * backend, size_t size); // allocates an owned buffer
GGML_API ggml_allocr_t ggml_allocr_new_measure_from_backend(struct ggml_backend * backend);
GGML_API struct ggml_backend_buffer * ggml_allocr_get_buffer(ggml_allocr_t alloc);
// tell the allocator to parse nodes following the order described in the list
// you should call this if your graph are optimized to execute out-of-order
GGML_API void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n);
GGML_API void ggml_allocr_set_parse_seq(ggml_allocr_t alloc, const int * list, int n);
GGML_API void ggml_allocr_free (struct ggml_allocr * alloc);
GGML_API bool ggml_allocr_is_measure (struct ggml_allocr * alloc);
GGML_API void ggml_allocr_reset (struct ggml_allocr * alloc);
GGML_API void ggml_allocr_alloc (struct ggml_allocr * alloc, struct ggml_tensor * tensor);
GGML_API size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph);
GGML_API size_t ggml_allocr_max_size (struct ggml_allocr * alloc);
GGML_API void ggml_allocr_free (ggml_allocr_t alloc);
GGML_API bool ggml_allocr_is_measure (ggml_allocr_t alloc);
GGML_API void ggml_allocr_reset (ggml_allocr_t alloc);
GGML_API void ggml_allocr_alloc (ggml_allocr_t alloc, struct ggml_tensor * tensor);
GGML_API size_t ggml_allocr_max_size (ggml_allocr_t alloc);
GGML_API size_t ggml_allocr_alloc_graph_n(
struct ggml_allocr * alloc,
struct ggml_cgraph ** graphs, int n_graphs,
struct ggml_tensor *** inputs, struct ggml_tensor *** outputs);
GGML_API size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph);
//
// ggml-backend v2 API
//
// Seperate tensor and graph allocator objects
// This is necessary for multi-backend allocation because the graph allocator needs to use multiple tensor allocators
// The original API is kept as a wrapper around the new API
// Tensor allocator
typedef struct ggml_tallocr * ggml_tallocr_t;
GGML_API ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment);
GGML_API ggml_tallocr_t ggml_tallocr_new_measure(size_t alignment);
GGML_API ggml_tallocr_t ggml_tallocr_new_from_buffer(struct ggml_backend_buffer * buffer);
GGML_API ggml_tallocr_t ggml_tallocr_new_from_backend(struct ggml_backend * backend, size_t size); // allocates an owned buffer
GGML_API ggml_tallocr_t ggml_tallocr_new_measure_from_backend(struct ggml_backend * backend);
GGML_API struct ggml_backend_buffer * ggml_tallocr_get_buffer(ggml_tallocr_t talloc);
GGML_API void ggml_tallocr_free (ggml_tallocr_t talloc);
GGML_API bool ggml_tallocr_is_measure (ggml_tallocr_t talloc);
GGML_API void ggml_tallocr_reset (ggml_tallocr_t talloc);
GGML_API void ggml_tallocr_alloc (ggml_tallocr_t talloc, struct ggml_tensor * tensor);
GGML_API size_t ggml_tallocr_max_size (ggml_tallocr_t talloc);
// Graph allocator
typedef struct ggml_gallocr * ggml_gallocr_t;
GGML_API ggml_gallocr_t ggml_gallocr_new(void);
GGML_API void ggml_gallocr_free(ggml_gallocr_t galloc);
GGML_API void ggml_gallocr_set_parse_seq(ggml_gallocr_t galloc, const int * list, int n);
GGML_API size_t ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, ggml_tallocr_t talloc, struct ggml_cgraph * graph);
// Allocate tensors from the allocators given by the hash table
GGML_API void ggml_gallocr_alloc_graph_n(
ggml_gallocr_t galloc,
struct ggml_cgraph * graph,
struct ggml_hash_set hash_set,
ggml_tallocr_t * hash_node_talloc);
#ifdef __cplusplus
}

87
ggml-backend-impl.h Normal file
View file

@ -0,0 +1,87 @@
#pragma once
// ggml-backend internal header
#include "ggml-backend.h"
#ifdef __cplusplus
extern "C" {
#endif
//
// Backend buffer
//
typedef void * ggml_backend_buffer_context_t;
struct ggml_backend_buffer_i {
void (*free_buffer) (ggml_backend_buffer_t buffer);
void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer
size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback
void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback
void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback
};
struct ggml_backend_buffer {
struct ggml_backend_buffer_i iface;
ggml_backend_t backend;
ggml_backend_buffer_context_t context;
size_t size;
};
GGML_API ggml_backend_buffer_t ggml_backend_buffer_init(
struct ggml_backend * backend,
struct ggml_backend_buffer_i iface,
ggml_backend_buffer_context_t context,
size_t size);
//
// Backend
//
typedef void * ggml_backend_context_t;
struct ggml_backend_i {
const char * (*get_name)(ggml_backend_t backend);
void (*free)(ggml_backend_t backend);
// buffer allocation
ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size);
// get buffer alignment
size_t (*get_alignment)(ggml_backend_t backend);
// tensor data access
// these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize
void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
void (*synchronize) (ggml_backend_t backend);
// (optional) copy tensor between different backends, allow for single-copy tranfers
void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
// compute graph with a plan
ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
// compute graph without a plan
void (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
// check if the backend supports an operation
bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
};
struct ggml_backend {
struct ggml_backend_i iface;
ggml_backend_context_t context;
};
#ifdef __cplusplus
}
#endif

591
ggml-backend.c
View file

@ -1,7 +1,9 @@
#include "ggml-backend.h"
#include "ggml-backend-impl.h"
#include "ggml-alloc.h"
#include "ggml-impl.h"
#include <assert.h>
#include <limits.h>
#include <stdarg.h>
#include <stdio.h>
#include <stdlib.h>
@ -33,6 +35,10 @@ ggml_backend_buffer_t ggml_backend_buffer_init(
}
void ggml_backend_buffer_free(ggml_backend_buffer_t buffer) {
if (buffer == NULL) {
return;
}
if (buffer->iface.free_buffer != NULL) {
buffer->iface.free_buffer(buffer);
}
@ -43,15 +49,20 @@ size_t ggml_backend_buffer_get_alignment(ggml_backend_buffer_t buffer) {
return ggml_backend_get_alignment(buffer->backend);
}
void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
return buffer->iface.get_base(buffer);
}
size_t ggml_backend_buffer_get_size(ggml_backend_buffer_t buffer) {
return buffer->size;
}
void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) {
void * base = buffer->iface.get_base(buffer);
GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL");
return base;
}
size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
// get_alloc_size is optional, defaults to ggml_nbytes
if (buffer->iface.get_alloc_size) {
return buffer->iface.get_alloc_size(buffer, tensor);
}
@ -59,12 +70,14 @@ size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct g
}
void ggml_backend_buffer_init_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
// init_tensor is optional
if (buffer->iface.init_tensor) {
buffer->iface.init_tensor(buffer, tensor);
}
}
void ggml_backend_buffer_free_tensor(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) {
// free_tensor is optional
if (buffer->iface.free_tensor) {
buffer->iface.free_tensor(buffer, tensor);
}
@ -73,14 +86,21 @@ void ggml_backend_buffer_free_tensor(ggml_backend_buffer_t buffer, struct ggml_t
// backend
ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor) {
return tensor->buffer->backend;
return tensor->buffer ? tensor->buffer->backend : NULL;
}
const char * ggml_backend_name(ggml_backend_t backend) {
if (backend == NULL) {
return "NULL";
}
return backend->iface.get_name(backend);
}
void ggml_backend_free(ggml_backend_t backend) {
if (backend == NULL) {
return;
}
backend->iface.free(backend);
}
@ -101,13 +121,23 @@ void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * dat
}
void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
ggml_get_backend(tensor)->iface.set_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
ggml_get_backend(tensor)->iface.synchronize(ggml_get_backend(tensor));
ggml_backend_t backend = ggml_get_backend(tensor);
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(backend != NULL && "tensor backend not set");
backend->iface.set_tensor_async(backend, tensor, data, offset, size);
backend->iface.synchronize(backend);
}
void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
ggml_get_backend(tensor)->iface.get_tensor_async(ggml_get_backend(tensor), tensor, data, offset, size);
ggml_get_backend(tensor)->iface.synchronize(ggml_get_backend(tensor));
ggml_backend_t backend = ggml_get_backend(tensor);
GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
GGML_ASSERT(backend != NULL && "tensor backend not set");
backend->iface.get_tensor_async(backend, tensor, data, offset, size);
backend->iface.synchronize(backend);
}
void ggml_backend_synchronize(ggml_backend_t backend) {
@ -156,7 +186,7 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst
//printf("dst: %s ne: [%d %d %d %d] nb: [%d %d %d %d]\n", dst->name, (int)dst->ne[0], (int)dst->ne[1], (int)dst->ne[2], (int)dst->ne[3], (int)dst->nb[0], (int)dst->nb[1], (int)dst->nb[2], (int)dst->nb[3]);
GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
// printf("cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src));
// fprintf(stderr, "cpy tensor %s from %s to %s (%lu bytes)\n", src->name, ggml_backend_name(src->backend), ggml_backend_name(dst->backend), ggml_nbytes(src));
if (src == dst) {
return;
@ -234,6 +264,8 @@ static ggml_backend_buffer_t ggml_backend_cpu_alloc_buffer(ggml_backend_t backen
size += TENSOR_ALIGNMENT; // malloc may return an address that is not aligned
void * data = malloc(size); // TODO: maybe use GGML_ALIGNED_MALLOC?
GGML_ASSERT(data != NULL && "failed to allocate buffer");
return ggml_backend_buffer_init(backend, cpu_backend_buffer_i, data, size);
}
@ -271,8 +303,7 @@ static void ggml_backend_cpu_cpy_tensor_from(ggml_backend_t backend, struct ggml
}
static void ggml_backend_cpu_cpy_tensor_to(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
// for a backend such as CUDA that can queue async calls, it is ok to do this asynchronously, but it may not be the case for other backends
ggml_backend_tensor_set_async(dst, src->data, 0, ggml_nbytes(src));
ggml_backend_tensor_set(dst, src->data, 0, ggml_nbytes(src));
UNUSED(backend);
}
@ -383,3 +414,537 @@ void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads) {
ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size) {
return ggml_backend_buffer_init(backend_cpu, cpu_backend_buffer_i_from_ptr, ptr, size);
}
// scheduler
#define GGML_MAX_BACKENDS 4
#define GGML_MAX_SPLITS 256
#define GGML_MAX_SPLIT_INPUTS 16
struct ggml_backend_sched_split {
ggml_tallocr_t tallocr;
int i_start;
int i_end;
struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS];
int n_inputs;
struct ggml_cgraph * graph;
};
struct ggml_backend_sched {
int n_backends;
ggml_backend_t backends[GGML_MAX_BACKENDS];
ggml_tallocr_t tallocs[GGML_MAX_BACKENDS];
ggml_gallocr_t galloc;
struct ggml_hash_set hash_set;
ggml_tallocr_t * node_talloc; // [hash_set.size]
struct ggml_tensor * (* node_copies)[GGML_MAX_BACKENDS]; // [hash_set.size][GGML_MAX_BACKENDS]
struct ggml_cgraph * graph;
struct ggml_backend_sched_split splits[GGML_MAX_SPLITS];
int n_splits;
struct ggml_context * ctx;
// align context_buffer to GGML_MEM_ALIGN
#ifdef _MSC_VER
__declspec(align(GGML_MEM_ALIGN))
#else
__attribute__((aligned(GGML_MEM_ALIGN)))
#endif
char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*sizeof(struct ggml_tensor) + GGML_MAX_SPLITS*sizeof(struct ggml_cgraph)];
};
#define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node)
#define node_allocr(node) sched->node_talloc[hash_id(node)]
static bool ggml_is_view_op(enum ggml_op op) {
return op == GGML_OP_VIEW || op == GGML_OP_RESHAPE || op == GGML_OP_PERMUTE || op == GGML_OP_TRANSPOSE;
}
// returns the priority of the backend, lower is better
static int sched_backend_prio(ggml_backend_sched_t sched, ggml_backend_t backend) {
for (int i = 0; i < sched->n_backends; i++) {
if (sched->backends[i] == backend) {
return i;
}
}
return INT_MAX;
}
static int sched_allocr_prio(ggml_backend_sched_t sched, ggml_tallocr_t allocr) {
for (int i = 0; i < sched->n_backends; i++) {
if (sched->tallocs[i] == allocr) {
return i;
}
}
return INT_MAX;
}
// returns the backend that should be used for the node based on the current locations
char causes[GGML_DEFAULT_GRAPH_SIZE*4 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug, remove
static ggml_backend_t sched_backend_from_cur(ggml_backend_sched_t sched, struct ggml_tensor * node) {
// if the dst tensor is already allocated in a buffer, we must assume that it is critical to keep it there
// ie. kv cache updates
// note that this doesn't allow fallback to CPU. need to add output tensors to the splits to copy the data back to the original backend.
// dst
ggml_backend_t cur_backend = ggml_get_backend(node);
if (cur_backend != NULL) {
sprintf(causes[hash_id(node)], "1.dst");
return cur_backend;
}
// view_src
if (node->view_src != NULL && ggml_get_backend(node->view_src) != NULL) {
sprintf(causes[hash_id(node)], "1.vsrc");
return ggml_get_backend(node->view_src);
}
// src
int cur_prio = INT_MAX;
size_t cur_size = 0;
for (int i = 0; i < GGML_MAX_SRC; i++) {
const struct ggml_tensor * src = node->src[i];
if (src == NULL) {
break;
}
ggml_backend_t src_backend = ggml_get_backend(src);
if (src_backend != NULL) {
int src_prio = sched_backend_prio(sched, src_backend);
size_t src_size = ggml_nbytes(src);
if (src_prio < cur_prio && src_size >= cur_size) {
cur_prio = src_prio;
cur_size = src_size;
cur_backend = src_backend;
sprintf(causes[hash_id(node)], "1.src%d", i);
}
}
}
return cur_backend;
}
static char * fmt_size(size_t size) {
static char buffer[128];
if (size >= 1024*1024) {
sprintf(buffer, "%zuM", size/1024/1024);
} else {
sprintf(buffer, "%zuK", size/1024);
}
return buffer;
}
static void sched_print_assignments(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
int cur_split = 0;
for (int i = 0; i < graph->n_nodes; i++) {
if (cur_split < sched->n_splits && i == sched->splits[cur_split].i_start) {
ggml_backend_t split_backend = ggml_tallocr_get_buffer(sched->splits[cur_split].tallocr)->backend;
fprintf(stderr, "\n## SPLIT #%d: %s # %d inputs: ", cur_split, ggml_backend_name(split_backend), sched->splits[cur_split].n_inputs);
for (int j = 0; j < sched->splits[cur_split].n_inputs; j++) {
fprintf(stderr, "[%s (%5.5s)] ", sched->splits[cur_split].inputs[j]->name, fmt_size(ggml_nbytes(sched->splits[cur_split].inputs[j])));
}
fprintf(stderr, "\n");
cur_split++;
}
struct ggml_tensor * node = graph->nodes[i];
if (ggml_is_view_op(node->op)) {
continue;
}
ggml_tallocr_t node_allocr = node_allocr(node);
ggml_backend_t node_backend = node_allocr ? ggml_tallocr_get_buffer(node_allocr)->backend : NULL;
fprintf(stderr, "node #%3d (%10.10s): %20.20s (%4.4s) [%4.4s %8.8s]:", i, ggml_op_name(node->op), node->name, fmt_size(ggml_nbytes(node)), node_allocr ? ggml_backend_name(node_backend) : "NULL", causes[hash_id(node)]);
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
break;
}
ggml_tallocr_t src_allocr = node_allocr(src);
ggml_backend_t src_backend = src_allocr ? ggml_tallocr_get_buffer(src_allocr)->backend : NULL;
fprintf(stderr, " %20.20s (%4.4s) [%4.4s %8.8s]", src->name, fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", causes[hash_id(src)]);
}
fprintf(stderr, "\n");
}
}
// creates a copy of the tensor with the same memory layout
static struct ggml_tensor * ggml_dup_tensor_layout(struct ggml_context * ctx, const struct ggml_tensor * tensor) {
struct ggml_tensor * dup = ggml_dup_tensor(ctx, tensor);
for (int i = 0; i < GGML_MAX_DIMS; i++) {
dup->nb[i] = tensor->nb[i];
}
return dup;
}
// assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend
// TODO: merge passes
static void sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
// reset state
size_t hash_size = sched->hash_set.size;
memset(sched->hash_set.keys, 0, sizeof(sched->hash_set.keys[0]) * hash_size);
memset(sched->node_talloc, 0, sizeof(sched->node_talloc[0]) * hash_size);
memset(sched->node_copies, 0, sizeof(sched->node_copies[0]) * hash_size);
sched->n_splits = 0;
struct ggml_init_params params = {
/*.mem_size = */ sizeof(sched->context_buffer),
/*.mem_buffer = */ sched->context_buffer,
/*.no_alloc = */ true
};
if (sched->ctx != NULL) {
ggml_free(sched->ctx);
}
sched->ctx = ggml_init(params);
// pass 1: assign backends to ops with allocated inputs
for (int i = 0; i < graph->n_leafs; i++) {
struct ggml_tensor * leaf = graph->leafs[i];
if (node_allocr(leaf) != NULL) {
// do not overwrite user assignments
continue;
}
ggml_backend_t leaf_backend = ggml_get_backend(leaf);
if (leaf_backend == NULL && leaf->view_src != NULL) {
leaf_backend = ggml_get_backend(leaf->view_src);
}
if (leaf_backend != NULL) {
node_allocr(leaf) = ggml_backend_sched_get_tallocr(sched, leaf_backend);
}
}
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
if (node_allocr(node) != NULL) {
// do not overwrite user assignments
continue;
}
ggml_backend_t node_backend = sched_backend_from_cur(sched, node);
if (node_backend != NULL) {
node_allocr(node) = ggml_backend_sched_get_tallocr(sched, node_backend);
}
}
//printf("PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
// pass 2: assign backends to ops from current assignments
// TODO:
// - reuse sched_backend_from_cur
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
ggml_tallocr_t node_allocr = node_allocr(node);
if (node_allocr == NULL) {
int cur_prio = INT_MAX;
size_t cur_size = 0;
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
break;
}
ggml_tallocr_t src_allocr = node_allocr(src);
if (src_allocr != NULL) {
int src_prio = sched_allocr_prio(sched, src_allocr);
size_t src_size = ggml_nbytes(src);
if (src_prio < cur_prio && src_size >= cur_size) {
cur_prio = src_prio;
cur_size = src_size;
node_allocr = src_allocr;
sprintf(causes[hash_id(node)], "2.src%d", j);
}
}
}
if (node_allocr != NULL) {
node_allocr(node) = node_allocr;
}
}
}
//printf("PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
// pass 3: assign backends to remaining src from dst (should only be leafs)
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
ggml_tallocr_t node_allocr = node_allocr(node);
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
break;
}
ggml_tallocr_t src_allocr = node_allocr(src);
if (src_allocr == NULL) {
node_allocr(src) = node_allocr;
}
}
}
//printf("PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
// pass 4: split graph, find tensors that need to be copied
// TODO:
// - when switching from a less preferred backend to a more preferred backend, check if it is possible to move the switch to an earlier point for the same cost
// find first backend
int cur_split = 0;
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
if (node->view_src == NULL) {
sched->splits[0].tallocr = node_allocr(node);
break;
}
}
sched->splits[0].i_start = 0;
sched->splits[0].n_inputs = 0;
memset(sched->splits[0].inputs, 0, sizeof(sched->splits[0].inputs)); //HACK
ggml_tallocr_t cur_allocr = sched->splits[0].tallocr;
size_t cur_backend_id = sched_allocr_prio(sched, cur_allocr);
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
if (ggml_is_view_op(node->op)) {
continue;
}
ggml_tallocr_t node_allocr = node_allocr(node);
if (node_allocr != cur_allocr) {
sched->splits[cur_split].i_end = i;
cur_split++;
GGML_ASSERT(cur_split < GGML_MAX_SPLITS);
sched->splits[cur_split].tallocr = node_allocr;
sched->splits[cur_split].i_start = i;
sched->splits[cur_split].n_inputs = 0;
memset(sched->splits[cur_split].inputs, 0, sizeof(sched->splits[cur_split].inputs)); //HACK
cur_allocr = node_allocr;
cur_backend_id = sched_allocr_prio(sched, cur_allocr);
}
// find inputs that are not on the same backend
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
break;
}
ggml_tallocr_t src_allocr = node_allocr(src);
if (src_allocr != node_allocr) {
int n_inputs = sched->splits[cur_split].n_inputs++;
GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS);
sched->splits[cur_split].inputs[n_inputs] = (struct ggml_tensor *)src;
// create copies
size_t id = hash_id(src);
if (sched->node_copies[id][cur_backend_id] == NULL) {
struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
sched->node_copies[id][cur_backend_id] = tensor_copy;
node_allocr(tensor_copy) = cur_allocr;
ggml_backend_t backend = ggml_tallocr_get_buffer(cur_allocr)->backend;
ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name);
}
node->src[j] = sched->node_copies[id][cur_backend_id];
}
}
}
sched->splits[cur_split].i_end = graph->n_nodes;
sched->n_splits = cur_split + 1;
//fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph); fflush(stdout);
#if 1
// sanity check: all sources should have the same backend as the node
for (int i = 0; i < graph->n_nodes; i++) {
struct ggml_tensor * node = graph->nodes[i];
ggml_tallocr_t node_allocr = node_allocr(node);
if (node_allocr == NULL) {
fprintf(stderr, "!!!!!!! %s has no backend\n", node->name);
}
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (src == NULL) {
break;
}
ggml_tallocr_t src_allocr = node_allocr(src);
if (src_allocr != node_allocr /* && src_backend != NULL */) { // ignore nulls for now
fprintf(stderr, "!!!! %s has backend %s, src %d (%s) has backend %s\n",
node->name, node_allocr ? ggml_backend_name(ggml_tallocr_get_buffer(node_allocr)->backend) : "NULL",
j, src->name, src_allocr ? ggml_backend_name(ggml_tallocr_get_buffer(src_allocr)->backend) : "NULL");
}
}
}
#endif
// create copies of the graph for each split
// FIXME: avoid this copy, pass split inputs to ggml_gallocr_alloc_graph_n in some other way
struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_MAX_SPLIT_INPUTS, false);
for (int i = 0; i < sched->n_splits; i++) {
struct ggml_backend_sched_split * split = &sched->splits[i];
split->graph = ggml_graph_view(sched->ctx, graph, split->i_start, split->i_end);
// add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split
for (int j = 0; j < split->n_inputs; j++) {
struct ggml_tensor * input = split->inputs[j];
struct ggml_tensor * input_cpy = sched->node_copies[hash_id(input)][sched_allocr_prio(sched, split->tallocr)];
input_cpy->src[0] = input;
graph_copy->nodes[graph_copy->n_nodes++] = input_cpy;
}
for (int j = split->i_start; j < split->i_end; j++) {
graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j];
}
}
sched->graph = graph_copy;
}
static void sched_alloc_splits(ggml_backend_sched_t sched) {
ggml_gallocr_alloc_graph_n(
sched->galloc,
sched->graph,
sched->hash_set,
sched->node_talloc);
}
static void sched_compute_splits(ggml_backend_sched_t sched) {
uint64_t copy_us[GGML_MAX_BACKENDS] = {0};
uint64_t compute_us[GGML_MAX_BACKENDS] = {0};
struct ggml_backend_sched_split * splits = sched->splits;
for (int i = 0; i < sched->n_splits; i++) {
struct ggml_backend_sched_split * split = &splits[i];
ggml_backend_t split_backend = ggml_tallocr_get_buffer(split->tallocr)->backend;
int split_backend_id = sched_backend_prio(sched, split_backend);
// copy the input tensors to the split backend
uint64_t copy_start_us = ggml_time_us();
for (int j = 0; j < split->n_inputs; j++) {
struct ggml_tensor * input_cpy = sched->node_copies[hash_id(split->inputs[j])][sched_backend_prio(sched, split_backend)];
if (split->inputs[j]->buffer == NULL) {
if (split->inputs[j]->view_src == NULL) {
fprintf(stderr, "input %s has no buffer and no view_src\n", split->inputs[j]->name);
exit(1);
}
struct ggml_tensor * view = split->inputs[j];
view->backend = view->view_src->backend;
view->buffer = view->view_src->buffer;
view->data = (char *)view->view_src->data + view->view_offs;
ggml_backend_buffer_init_tensor(ggml_backend_sched_get_buffer(sched, view->buffer->backend), view);
}
if (input_cpy->buffer == NULL) {
fprintf(stderr, "input_cpy %s has no buffer\n", input_cpy->name);
exit(1);
}
GGML_ASSERT(split->inputs[j]->buffer->backend != input_cpy->buffer->backend);
GGML_ASSERT(input_cpy->buffer->backend == split_backend);
ggml_backend_tensor_copy(split->inputs[j], input_cpy);
}
// ggml_backend_synchronize(split_backend);
int64_t copy_end_us = ggml_time_us();
copy_us[split_backend_id] += copy_end_us - copy_start_us;
#if 0
char split_filename[GGML_MAX_NAME];
snprintf(split_filename, GGML_MAX_NAME, "split_%i_%s.dot", i, ggml_backend_name(split_backend));
ggml_graph_dump_dot(split->graph, NULL, split_filename);
#endif
uint64_t compute_start_us = ggml_time_us();
ggml_backend_graph_compute(split_backend, split->graph);
// ggml_backend_synchronize(split_backend);
uint64_t compute_end_us = ggml_time_us();
compute_us[split_backend_id] += compute_end_us - compute_start_us;
}
#if 0
// per-backend timings
fprintf(stderr, "sched_compute_splits times (%d splits):\n", sched->n_splits);
for (int i = 0; i < sched->n_backends; i++) {
if (copy_us[i] > 0 || compute_us[i] > 0) {
fprintf(stderr, "\t%5.5s: %lu us copy, %lu us compute\n", ggml_backend_name(sched->backends[i]), copy_us[i], compute_us[i]);
}
}
#endif
}
static void sched_reset(ggml_backend_sched_t sched) {
for (int i = 0; i < sched->n_backends; i++) {
ggml_tallocr_reset(sched->tallocs[i]);
}
}
ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends) {
GGML_ASSERT(n_backends <= GGML_MAX_BACKENDS);
struct ggml_backend_sched * sched = malloc(sizeof(struct ggml_backend_sched));
memset(sched, 0, sizeof(struct ggml_backend_sched));
fprintf(stderr, "ggml_backend_sched size: %lu KB\n", sizeof(struct ggml_backend_sched)/1024);
sched->n_backends = n_backends;
for (int i = 0; i < n_backends; i++) {
sched->backends[i] = backends[i];
}
sched->galloc = ggml_gallocr_new();
// init measure allocs for each backend
for (int i = 0; i < n_backends; i++) {
sched->tallocs[i] = ggml_tallocr_new_measure_from_backend(backends[i]);
}
return sched;
}
void ggml_backend_sched_free(ggml_backend_sched_t sched) {
if (sched == NULL) {
return;
}
for (int i = 0; i < sched->n_backends; i++) {
ggml_tallocr_free(sched->tallocs[i]);
}
ggml_gallocr_free(sched->galloc);
free(sched->hash_set.keys);
free(sched->node_talloc);
free(sched->node_copies);
free(sched);
}
void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
// initialize hash tables
size_t hash_size = measure_graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS;
sched->hash_set.size = hash_size;
sched->hash_set.keys = malloc(sizeof(sched->hash_set.keys[0]) * hash_size);
sched->node_talloc = malloc(sizeof(sched->node_talloc[0]) * hash_size);
sched->node_copies = malloc(sizeof(sched->node_copies[0]) * hash_size);
sched_split_graph(sched, measure_graph);
sched_alloc_splits(sched);
// allocate buffers and reset allocators
for (int i = 0; i < sched->n_backends; i++) {
size_t size = ggml_tallocr_max_size(sched->tallocs[i]);
ggml_tallocr_free(sched->tallocs[i]);
sched->tallocs[i] = ggml_tallocr_new_from_backend(sched->backends[i], size);
}
sched_reset(sched);
}
void ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
GGML_ASSERT(sched->hash_set.size >= graph->visited_hash_table.size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS);
sched_split_graph(sched, graph);
sched_alloc_splits(sched);
sched_compute_splits(sched);
sched_reset(sched);
}
ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend) {
int backend_index = sched_backend_prio(sched, backend);
return sched->tallocs[backend_index];
}
ggml_backend_buffer_t ggml_backend_sched_get_buffer(ggml_backend_sched_t sched, ggml_backend_t backend) {
int backend_index = sched_backend_prio(sched, backend);
return ggml_tallocr_get_buffer(sched->tallocs[backend_index]);
}
void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
int backend_index = sched_backend_prio(sched, backend);
GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
node_allocr(node) = sched->tallocs[backend_index];
}

151
ggml-backend.h
View file

@ -1,51 +1,20 @@
#pragma once
#include "ggml.h"
#include "ggml-alloc.h"
#ifdef __cplusplus
extern "C" {
#endif
struct ggml_backend;
//
// Backend buffer
//
struct ggml_backend_buffer;
// type-erased backend-specific types / wrappers
typedef void * ggml_backend_context_t;
typedef void * ggml_backend_graph_plan_t;
typedef void * ggml_backend_buffer_context_t;
// avoid accessing internals of these types
typedef struct ggml_backend * ggml_backend_t;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
//
// backend buffer
//
struct ggml_backend_buffer_i {
void (*free_buffer) (ggml_backend_buffer_t buffer);
void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer
size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback
void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback
void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback
};
// TODO: hide behind API
struct ggml_backend_buffer {
struct ggml_backend_buffer_i iface;
ggml_backend_t backend;
ggml_backend_buffer_context_t context;
size_t size;
};
// backend buffer functions
GGML_API ggml_backend_buffer_t ggml_backend_buffer_init(
struct ggml_backend * backend,
struct ggml_backend_buffer_i iface,
ggml_backend_buffer_context_t context,
size_t size);
GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer);
@ -55,50 +24,13 @@ extern "C" {
GGML_API void ggml_backend_buffer_free_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
//
// backend
// Backend
//
struct ggml_backend_i {
const char * (*get_name)(ggml_backend_t backend);
struct ggml_backend;
typedef struct ggml_backend * ggml_backend_t;
typedef void * ggml_backend_graph_plan_t;
void (*free)(ggml_backend_t backend);
// buffer allocation
ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size);
// get buffer alignment
size_t (*get_alignment)(ggml_backend_t backend);
// tensor data access
// these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize
void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
void (*synchronize) (ggml_backend_t backend);
// (optional) copy tensor between different backends, allow for single-copy tranfers
void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
// compute graph with a plan
ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
void (*graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
void (*graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
// compute graph without a plan
void (*graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
// check if the backend supports an operation
bool (*supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
};
// TODO: hide behind API
struct ggml_backend {
struct ggml_backend_i iface;
ggml_backend_context_t context;
};
// backend helper functions
GGML_API ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor);
GGML_API const char * ggml_backend_name(ggml_backend_t backend);
@ -133,11 +65,72 @@ extern "C" {
GGML_API ggml_backend_t ggml_backend_cpu_init(void);
GGML_API bool ggml_backend_is_cpu(ggml_backend_t backend);
GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
// Create a backend buffer from an existing pointer
GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size);
//
// Backend scheduler
//
// The backend scheduler allows for multiple backends to be used together
// Handles compute buffer allocation, assignment of tensors to backends, and copying of tensors between backends
// The backends are selected based on:
// - the backend that supports the operation
// - the location of the pre-allocated tensors (e.g. the weights)
/*
Example usage:
sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, num_backends);
// sched is initialized with measure allocators and cannot be used until allocated with a measure graph
// initialize buffers from a measure graph
measure_graph = build_graph(sched); // use the allocr to allocate inputs as needed
// in build_graph:
build_graph(...) {
// allocating tensors in a specific backend (optional, recommended: pre-allocate inputs in a different buffer)
alloc_cpu = ggml_backend_sched_get_allocr(sched, backend_cpu);
ggml_allocr_alloc(alloc_cpu, tensor);
// manually assigning nodes to a backend (optional, shouldn't be needed in most cases)
struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
ggml_backend_sched_set_node_backend(sched, node, backend_gpu);
}
// allocate backend buffers from measure graph
ggml_backend_sched_init_measure(sched, measure_graph);
// the scheduler is now ready to compute graphs
// compute
graph = build_graph(sched);
ggml_backend_sched_graph_compute(sched, graph);
*/
struct ggml_backend_sched;
typedef struct ggml_backend_sched * ggml_backend_sched_t;
// Initialize a backend scheduler
GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, int n_backends);
GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched);
// Initialize backend buffers from a measure graph
GGML_API void ggml_backend_sched_init_measure(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
GGML_API ggml_tallocr_t ggml_backend_sched_get_tallocr(ggml_backend_sched_t sched, ggml_backend_t backend);
GGML_API ggml_backend_buffer_t ggml_backend_sched_get_buffer (ggml_backend_sched_t sched, ggml_backend_t backend);
GGML_API void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
// Allocate a graph on the backend scheduler
GGML_API void ggml_backend_sched_graph_compute(
ggml_backend_sched_t sched,
struct ggml_cgraph * graph);
#ifdef __cplusplus
}
#endif

194
ggml-cuda.cu
View file

@ -81,6 +81,7 @@
#include "ggml-cuda.h"
#include "ggml.h"
#include "ggml-backend-impl.h"
#define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
#define CC_VOLTA 700
@ -433,6 +434,8 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
#define CUDA_MUL_BLOCK_SIZE 256
#define CUDA_GELU_BLOCK_SIZE 256
#define CUDA_SILU_BLOCK_SIZE 256
#define CUDA_RELU_BLOCK_SIZE 256
#define CUDA_SQR_BLOCK_SIZE 256
#define CUDA_CPY_BLOCK_SIZE 32
#define CUDA_SCALE_BLOCK_SIZE 256
#define CUDA_CLAMP_BLOCK_SIZE 256
@ -554,6 +557,24 @@ static __global__ void silu_f32(const float * x, float * dst, const int k) {
dst[i] = x[i] / (1.0f + expf(-x[i]));
}
static __global__ void relu_f32(const float * x, float * dst, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= k) {
return;
}
dst[i] = fmaxf(x[i], 0);
}
static __global__ void sqr_f32(const float * x, float * dst, const int k) {
const int i = blockDim.x*blockIdx.x + threadIdx.x;
if (i >= k) {
return;
}
dst[i] = x[i] * x[i];
}
static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) {
#pragma unroll
for (int mask = 16; mask > 0; mask >>= 1) {
@ -4469,6 +4490,13 @@ static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) {
*dsti = __float2half(*xi);
}
static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) {
const half * xi = (const half *) cxi;
half * dsti = (half *) cdsti;
*dsti = *xi;
}
template <cpy_kernel_t cpy_1>
static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
@ -4722,6 +4750,25 @@ static __global__ void clamp_f32(const float * x, float * dst, const float min,
dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]);
}
static __global__ void im2col_f32_f16(
const float * x, half * dst,
int ofs0, int ofs1, int IW, int IH, int CHW,
int s0, int s1, int p0, int p1, int d0, int d1) {
const int iiw = blockIdx.z * s0 + threadIdx.z * d0 - p0;
const int iih = blockIdx.y * s1 + threadIdx.y * d1 - p1;
const int offset_dst =
(threadIdx.x * gridDim.y * gridDim.z + blockIdx.y * gridDim.z + blockIdx.z) * CHW +
(blockIdx.x * (blockDim.y * blockDim.z) + threadIdx.y * blockDim.z + threadIdx.z);
if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
dst[offset_dst] = __float2half(0.0f);
} else {
const int offset_src = threadIdx.x * ofs0 + blockIdx.x * ofs1;
dst[offset_dst] = __float2half(x[offset_src + iih * IW + iiw]);
}
}
template<int qk, int qr, dequantize_kernel_t dq>
static void get_rows_cuda(const void * x, const int32_t * y, float * dst, const int nrows, const int ncols, cudaStream_t stream) {
const dim3 block_dims(CUDA_GET_ROWS_BLOCK_SIZE, 1, 1);
@ -4760,6 +4807,16 @@ static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_
silu_f32<<<num_blocks, CUDA_SILU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
}
static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE;
relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k);
}
static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_SQR_BLOCK_SIZE - 1) / CUDA_SQR_BLOCK_SIZE;
sqr_f32<<<num_blocks, CUDA_SQR_BLOCK_SIZE, 0, stream>>>(x, dst, k);
}
static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) {
GGML_ASSERT(ncols % WARP_SIZE == 0);
if (ncols < 1024) {
@ -5612,6 +5669,16 @@ static void ggml_cpy_f32_f16_cuda(
(cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
}
static void ggml_cpy_f16_f16_cuda(
const char * cx, char * cdst, const int ne,
const int ne00, const int ne01, const int nb00, const int nb01, const int nb02,
const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) {
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
cpy_f32_f16<cpy_1_f16_f16><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
(cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12);
}
static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) {
const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE;
scale_f32<<<num_blocks, CUDA_SCALE_BLOCK_SIZE, 0, stream>>>(x, dst, scale, k);
@ -5695,6 +5762,15 @@ static void soft_max_f32_cuda(const float * x, float * dst, const int ncols_x, c
soft_max_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x);
}
static void im2col_f32_f16_cuda(const float * x, half * dst,
int OH, int IW, int IH, int OW, int IC,
int KH, int KW, int N, int ofs0, int ofs1,
int s0, int s1, int p0, int p1, int d0, int d1, cudaStream_t stream) {
dim3 block_nums(IC, OH, OW);
dim3 block_dims(N, KH, KW);
im2col_f32_f16<<<block_nums, block_dims, 0, stream>>>(x, dst, ofs0, ofs1, IW, IH, (IC * KH * KW), s0, s1, p0, p1, d0, d1);
}
// buffer pool for cuda
#define MAX_CUDA_BUFFERS 256
@ -6117,6 +6193,34 @@ inline void ggml_cuda_op_silu(
(void) src1_dd;
}
inline void ggml_cuda_op_relu(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
relu_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
(void) src1;
(void) dst;
(void) src1_dd;
}
inline void ggml_cuda_op_sqr(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F32);
sqr_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream);
(void) src1;
(void) dst;
(void) src1_dd;
}
inline void ggml_cuda_op_norm(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
@ -6452,8 +6556,7 @@ inline void ggml_cuda_op_mul_mat_cublas(
src1_as_f16 = (half *) ggml_cuda_pool_malloc(ne * sizeof(half), &src1_as);
to_fp16_cuda(src1_ddf_i, src1_as_f16, ne, stream);
}
const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddq_i : src1_as_f16;
const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16;
size_t dst_as = 0;
half * dst_f16 = (half *) ggml_cuda_pool_malloc(row_diff*src1_ncols * sizeof(half), &dst_as);
@ -6628,6 +6731,45 @@ inline void ggml_cuda_op_alibi(
(void) src1_dd;
}
inline void ggml_cuda_op_im2col(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
GGML_ASSERT(src0->type == GGML_TYPE_F16);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F16);
const int32_t s0 = ((const int32_t*)(dst->op_params))[0];
const int32_t s1 = ((const int32_t*)(dst->op_params))[1];
const int32_t p0 = ((const int32_t*)(dst->op_params))[2];
const int32_t p1 = ((const int32_t*)(dst->op_params))[3];
const int32_t d0 = ((const int32_t*)(dst->op_params))[4];
const int32_t d1 = ((const int32_t*)(dst->op_params))[5];
const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1;
const int64_t N = src1->ne[is_2D ? 3 : 2];
const int64_t IC = src1->ne[is_2D ? 2 : 1];
const int64_t IH = is_2D ? src1->ne[1] : 1;
const int64_t IW = src1->ne[0];
const int64_t KH = is_2D ? src0->ne[1] : 1;
const int64_t KW = src0->ne[0];
const int64_t OH = is_2D ? dst->ne[2] : 1;
const int64_t OW = dst->ne[1];
const size_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4; // nb is byte offset, src is type float32
const size_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32
im2col_f32_f16_cuda(src1_dd, (half*) dst_dd,
OH, IW, IH, OW, IC, KH, KW, N,
ofs0, ofs1, s0, s1, p0, p1, d0, d1, main_stream);
(void) src0;
(void) src0_dd;
}
inline void ggml_cuda_op_diag_mask_inf(
const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst,
const float * src0_dd, const float * src1_dd, float * dst_dd, const cudaStream_t & main_stream) {
@ -7149,6 +7291,14 @@ static void ggml_cuda_silu(const ggml_tensor * src0, const ggml_tensor * src1, g
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_silu);
}
static void ggml_cuda_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_relu);
}
static void ggml_cuda_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_sqr);
}
static void ggml_cuda_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_norm);
}
@ -7535,6 +7685,9 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg
} else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) {
ggml_cpy_f32_f16_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02,
ne10, ne11, nb10, nb11, nb12, main_stream);
} else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) {
ggml_cpy_f16_f16_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02,
ne10, ne11, nb10, nb11, nb12, main_stream);
} else {
fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__,
ggml_type_name(src0->type), ggml_type_name(src1->type));
@ -7566,6 +7719,10 @@ static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1,
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi);
}
void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_im2col);
}
static void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
(void) src0;
(void) src1;
@ -7677,11 +7834,11 @@ static size_t g_temp_tensor_extra_index = 0;
static ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
if (g_temp_tensor_extras == nullptr) {
g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_MAX_NODES];
g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_DEFAULT_GRAPH_SIZE];
}
size_t alloc_index = g_temp_tensor_extra_index;
g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_MAX_NODES;
g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_DEFAULT_GRAPH_SIZE;
ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index];
memset(extra, 0, sizeof(*extra));
@ -7863,6 +8020,15 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
return false;
}
if (tensor->op == GGML_OP_MUL_MAT) {
if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) {
#ifndef NDEBUG
fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %d, src1->ne[3] = %d - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]);
#endif
return false;
}
}
switch (tensor->op) {
case GGML_OP_REPEAT:
func = ggml_cuda_repeat;
@ -7887,6 +8053,9 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
case GGML_UNARY_OP_SILU:
func = ggml_cuda_silu;
break;
case GGML_UNARY_OP_RELU:
func = ggml_cuda_relu;
break;
default:
return false;
} break;
@ -7905,6 +8074,9 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
case GGML_OP_SCALE:
func = ggml_cuda_scale;
break;
case GGML_OP_SQR:
func = ggml_cuda_sqr;
break;
case GGML_OP_CLAMP:
if (!any_on_device) {
return false;
@ -7935,6 +8107,9 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
case GGML_OP_ALIBI:
func = ggml_cuda_alibi;
break;
case GGML_OP_IM2COL:
func = ggml_cuda_im2col;
break;
default:
return false;
}
@ -7994,11 +8169,11 @@ struct ggml_backend_buffer_context_cuda {
ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
if (temp_tensor_extras == nullptr) {
temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_MAX_NODES];
temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_DEFAULT_GRAPH_SIZE];
}
size_t alloc_index = temp_tensor_extra_index;
temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_MAX_NODES;
temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_DEFAULT_GRAPH_SIZE;
ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index];
memset(extra, 0, sizeof(*extra));
@ -8084,7 +8259,12 @@ static ggml_backend_buffer_t ggml_backend_cuda_alloc_buffer(ggml_backend_t backe
ggml_cuda_set_device(g_main_device);
ggml_backend_buffer_context_cuda * ctx = new ggml_backend_buffer_context_cuda;
size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0
ggml_cuda_set_device(g_main_device);
CUDA_CHECK(cudaMalloc(&ctx->device, size));
return ggml_backend_buffer_init(backend, cuda_backend_buffer_interface, ctx, size);
}
@ -8151,6 +8331,8 @@ static void ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph
for (int i = 0; i < cgraph->n_nodes; i++) {
ggml_tensor * node = cgraph->nodes[i];
if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE)
continue;
assert(node->backend == GGML_BACKEND_GPU);
for (int j = 0; j < GGML_MAX_SRC; j++) {
if (node->src[j] != nullptr) {

20
ggml-impl.h
View file

@ -39,12 +39,6 @@ extern "C" {
#endif
#endif
#undef MIN
#undef MAX
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))
// 16-bit float
// on Arm, we use __fp16
// on x86, we use uint16_t
@ -230,7 +224,19 @@ inline static float ggml_lookup_fp16_to_fp32(ggml_fp16_t f) {
#endif
// TODO: backend v2 PR
#define GGML_HASHTABLE_FULL ((size_t)-1)
#define GGML_HASHTABLE_ALREADY_EXISTS ((size_t)-2)
bool ggml_hash_contains (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
// returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted
size_t ggml_hash_find (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
// returns GGML_HAHSHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
size_t ggml_hash_insert ( struct ggml_hash_set hash_set, struct ggml_tensor * key);
// return index, asserts if table is full
size_t ggml_hash_find_or_insert( struct ggml_hash_set hash_set, struct ggml_tensor * key);
#ifdef __cplusplus
}

2
ggml-metal.h
View file

@ -26,7 +26,7 @@
#include <stdbool.h>
// max memory buffers that can be mapped to the device
#define GGML_METAL_MAX_BUFFERS 16
#define GGML_METAL_MAX_BUFFERS 64
#define GGML_METAL_MAX_COMMAND_BUFFERS 32
struct ggml_tensor;

119
ggml-metal.m
View file

@ -1,5 +1,6 @@
#import "ggml-metal.h"
#import "ggml-backend-impl.h"
#import "ggml.h"
#import <Foundation/Foundation.h>
@ -23,7 +24,7 @@
#define UNUSED(x) (void)(x)
#define GGML_MAX_CONCUR (2*GGML_MAX_NODES)
#define GGML_MAX_CONCUR (2*GGML_DEFAULT_GRAPH_SIZE)
struct ggml_metal_buffer {
const char * name;
@ -85,6 +86,7 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(rms_norm);
GGML_METAL_DECL_KERNEL(norm);
GGML_METAL_DECL_KERNEL(mul_mv_f32_f32);
GGML_METAL_DECL_KERNEL(mul_mv_f16_f16);
GGML_METAL_DECL_KERNEL(mul_mv_f16_f32);
GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_1row);
GGML_METAL_DECL_KERNEL(mul_mv_f16_f32_l4);
@ -113,6 +115,7 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(rope_f32);
GGML_METAL_DECL_KERNEL(rope_f16);
GGML_METAL_DECL_KERNEL(alibi_f32);
GGML_METAL_DECL_KERNEL(im2col_f16);
GGML_METAL_DECL_KERNEL(cpy_f32_f16);
GGML_METAL_DECL_KERNEL(cpy_f32_f32);
GGML_METAL_DECL_KERNEL(cpy_f16_f16);
@ -125,7 +128,7 @@ struct ggml_metal_context {
// MSL code
// TODO: move the contents here when ready
// for now it is easier to work in a separate file
static NSString * const msl_library_source = @"see metal.metal";
//static NSString * const msl_library_source = @"see metal.metal";
// Here to assist with NSBundle Path Hack
@interface GGMLMetalClass : NSObject
@ -141,7 +144,8 @@ void ggml_metal_log_set_callback(ggml_log_callback log_callback, void * user_dat
ggml_metal_log_user_data = user_data;
}
static void ggml_metal_log(enum ggml_log_level level, const char* format, ...){
GGML_ATTRIBUTE_FORMAT(2, 3)
static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){
if (ggml_metal_log_callback != NULL) {
va_list args;
va_start(args, format);
@ -209,7 +213,13 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
} else {
GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
NSString * sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
NSString * sourcePath;
NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
if (ggmlMetalPathResources) {
sourcePath = [ggmlMetalPathResources stringByAppendingPathComponent:@"ggml-metal.metal"];
} else {
sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
}
if (sourcePath == nil) {
GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
sourcePath = @"ggml-metal.metal";
@ -280,6 +290,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(rms_norm);
GGML_METAL_ADD_KERNEL(norm);
GGML_METAL_ADD_KERNEL(mul_mv_f32_f32);
GGML_METAL_ADD_KERNEL(mul_mv_f16_f16);
GGML_METAL_ADD_KERNEL(mul_mv_f16_f32);
GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_1row);
GGML_METAL_ADD_KERNEL(mul_mv_f16_f32_l4);
@ -310,6 +321,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
GGML_METAL_ADD_KERNEL(rope_f32);
GGML_METAL_ADD_KERNEL(rope_f16);
GGML_METAL_ADD_KERNEL(alibi_f32);
GGML_METAL_ADD_KERNEL(im2col_f16);
GGML_METAL_ADD_KERNEL(cpy_f32_f16);
GGML_METAL_ADD_KERNEL(cpy_f32_f32);
GGML_METAL_ADD_KERNEL(cpy_f16_f16);
@ -328,7 +340,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
// https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
for (int i = MTLGPUFamilyApple1 + 20; i >= MTLGPUFamilyApple1; --i) {
if ([ctx->device supportsFamily:i]) {
GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - MTLGPUFamilyApple1 + 1, i);
GGML_METAL_LOG_INFO("%s: GPU family: MTLGPUFamilyApple%d (%d)\n", __func__, i - (int) MTLGPUFamilyApple1 + 1, i);
break;
}
}
@ -379,6 +391,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
GGML_METAL_DEL_KERNEL(rms_norm);
GGML_METAL_DEL_KERNEL(norm);
GGML_METAL_DEL_KERNEL(mul_mv_f32_f32);
GGML_METAL_DEL_KERNEL(mul_mv_f16_f16);
GGML_METAL_DEL_KERNEL(mul_mv_f16_f32);
GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_1row);
GGML_METAL_DEL_KERNEL(mul_mv_f16_f32_l4);
@ -409,6 +422,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
GGML_METAL_DEL_KERNEL(rope_f32);
GGML_METAL_DEL_KERNEL(rope_f16);
GGML_METAL_DEL_KERNEL(alibi_f32);
GGML_METAL_DEL_KERNEL(im2col_f16);
GGML_METAL_DEL_KERNEL(cpy_f32_f16);
GGML_METAL_DEL_KERNEL(cpy_f32_f32);
GGML_METAL_DEL_KERNEL(cpy_f16_f16);
@ -466,6 +480,10 @@ static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru
const int64_t tsize = ggml_nbytes(t);
if (t->buffer && t->buffer->backend && t->buffer->backend->context) {
ctx = t->buffer->backend->context;
}
// find the view that contains the tensor fully
for (int i = 0; i < ctx->n_buffers; ++i) {
const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data;
@ -566,7 +584,7 @@ bool ggml_metal_add_buffer(
ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) {
GGML_METAL_LOG_WARN(", warning: current allocated size is greater than the recommended max working set size\n", __func__);
GGML_METAL_LOG_WARN("%s: warning: current allocated size is greater than the recommended max working set size\n", __func__);
} else {
GGML_METAL_LOG_INFO("\n");
}
@ -744,6 +762,20 @@ void ggml_metal_graph_compute(
struct ggml_tensor * src1 = gf->nodes[i]->src[1];
struct ggml_tensor * dst = gf->nodes[i];
switch (dst->op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_TRANSPOSE:
case GGML_OP_PERMUTE:
{
// noop -> next node
} continue;
default:
{
} break;
}
const int64_t ne00 = src0 ? src0->ne[0] : 0;
const int64_t ne01 = src0 ? src0->ne[1] : 0;
const int64_t ne02 = src0 ? src0->ne[2] : 0;
@ -797,14 +829,6 @@ void ggml_metal_graph_compute(
//}
switch (dst->op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_VIEW:
case GGML_OP_TRANSPOSE:
case GGML_OP_PERMUTE:
{
// noop
} break;
case GGML_OP_CONCAT:
{
const int64_t nb = ne00;
@ -1017,7 +1041,7 @@ void ggml_metal_graph_compute(
[encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2];
[encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3];
[encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
[encoder setThreadgroupMemoryLength:MAX(16, nth/32*sizeof(float)) atIndex:0];
[encoder setThreadgroupMemoryLength:GGML_PAD(nth/32*sizeof(float), 16) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
@ -1126,6 +1150,7 @@ void ggml_metal_graph_compute(
switch (src0t) {
case GGML_TYPE_F32:
{
GGML_ASSERT(src1t == GGML_TYPE_F32);
[encoder setComputePipelineState:ctx->pipeline_mul_mv_f32_f32];
nrows = 4;
} break;
@ -1133,6 +1158,7 @@ void ggml_metal_graph_compute(
{
nth0 = 32;
nth1 = 1;
if (src1t == GGML_TYPE_F32) {
if (ne11 * ne12 < 4) {
[encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32_1row];
} else if (ne00 >= 128 && ne01 >= 8 && ne00%4 == 0) {
@ -1142,6 +1168,10 @@ void ggml_metal_graph_compute(
[encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f32];
nrows = 4;
}
} else {
[encoder setComputePipelineState:ctx->pipeline_mul_mv_f16_f16];
nrows = 4;
}
} break;
case GGML_TYPE_Q4_0:
{
@ -1329,7 +1359,7 @@ void ggml_metal_graph_compute(
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
[encoder setBytes:&eps length:sizeof( float) atIndex:4];
[encoder setThreadgroupMemoryLength:nth/32*sizeof(float) atIndex:0];
[encoder setThreadgroupMemoryLength:GGML_PAD(nth/32*sizeof(float), 16) atIndex:0];
const int64_t nrows = ggml_nrows(src0);
@ -1348,7 +1378,7 @@ void ggml_metal_graph_compute(
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
[encoder setBytes:&eps length:sizeof( float) atIndex:4];
[encoder setThreadgroupMemoryLength:MAX(16, nth*sizeof(float)) atIndex:0];
[encoder setThreadgroupMemoryLength:GGML_PAD(nth*sizeof(float), 16) atIndex:0];
const int64_t nrows = ggml_nrows(src0);
@ -1403,8 +1433,7 @@ void ggml_metal_graph_compute(
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];
// skip 3, n_ctx, used in GLM RoPE, unimplemented in metal
const int n_orig_ctx = ((int32_t *) dst->op_params)[4];
const int n_orig_ctx = ((int32_t *) dst->op_params)[3];
float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow;
memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float));
@ -1452,6 +1481,58 @@ void ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_IM2COL:
{
GGML_ASSERT(src0->type == GGML_TYPE_F16);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT( dst->type == GGML_TYPE_F16);
const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
const int32_t s1 = ((const int32_t *)(dst->op_params))[1];
const int32_t p0 = ((const int32_t *)(dst->op_params))[2];
const int32_t p1 = ((const int32_t *)(dst->op_params))[3];
const int32_t d0 = ((const int32_t *)(dst->op_params))[4];
const int32_t d1 = ((const int32_t *)(dst->op_params))[5];
const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1;
const int32_t N = src1->ne[is_2D ? 3 : 2];
const int32_t IC = src1->ne[is_2D ? 2 : 1];
const int32_t IH = is_2D ? src1->ne[1] : 1;
const int32_t IW = src1->ne[0];
const int32_t KH = is_2D ? src0->ne[1] : 1;
const int32_t KW = src0->ne[0];
const int32_t OH = is_2D ? dst->ne[2] : 1;
const int32_t OW = dst->ne[1];
const int32_t CHW = IC * KH * KW;
const int32_t ofs0 = src1->nb[is_2D ? 3 : 2] / 4;
const int32_t ofs1 = src1->nb[is_2D ? 2 : 1] / 4;
switch (src0->type) {
case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break;
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_im2col_f16]; break;
default: GGML_ASSERT(false);
};
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ofs0 length:sizeof( int32_t) atIndex:2];
[encoder setBytes:&ofs1 length:sizeof( int32_t) atIndex:3];
[encoder setBytes:&IW length:sizeof( int32_t) atIndex:4];
[encoder setBytes:&IH length:sizeof( int32_t) atIndex:5];
[encoder setBytes:&CHW length:sizeof( int32_t) atIndex:6];
[encoder setBytes:&s0 length:sizeof( int32_t) atIndex:7];
[encoder setBytes:&s1 length:sizeof( int32_t) atIndex:8];
[encoder setBytes:&p0 length:sizeof( int32_t) atIndex:9];
[encoder setBytes:&p1 length:sizeof( int32_t) atIndex:10];
[encoder setBytes:&d0 length:sizeof( int32_t) atIndex:11];
[encoder setBytes:&d1 length:sizeof( int32_t) atIndex:12];
[encoder dispatchThreadgroups:MTLSizeMake(IC, OH, OW) threadsPerThreadgroup:MTLSizeMake(N, KH, KW)];
} break;
case GGML_OP_DUP:
case GGML_OP_CPY:
case GGML_OP_CONT:

106
ggml-metal.metal
View file

@ -844,6 +844,79 @@ kernel void kernel_mul_mv_f32_f32(
}
}
#define N_F16_F16 4
kernel void kernel_mul_mv_f16_f16(
device const char * src0,
device const char * src1,
device float * dst,
constant int64_t & ne00,
constant int64_t & ne01,
constant int64_t & ne02,
constant uint64_t & nb00,
constant uint64_t & nb01,
constant uint64_t & nb02,
constant int64_t & ne10,
constant int64_t & ne11,
constant int64_t & ne12,
constant uint64_t & nb10,
constant uint64_t & nb11,
constant uint64_t & nb12,
constant int64_t & ne0,
constant int64_t & ne1,
uint3 tgpig[[threadgroup_position_in_grid]],
uint tiisg[[thread_index_in_simdgroup]]) {
const int64_t r0 = tgpig.x;
const int64_t rb = tgpig.y*N_F16_F16;
const int64_t im = tgpig.z;
device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02);
if (ne00 < 128) {
for (int row = 0; row < N_F16_F16; ++row) {
int r1 = rb + row;
if (r1 >= ne11) {
break;
}
device const half * y = (device const half *) (src1 + r1*nb11 + im*nb12);
float sumf = 0;
for (int i = tiisg; i < ne00; i += 32) {
sumf += (half) x[i] * (half) y[i];
}
float all_sum = simd_sum(sumf);
if (tiisg == 0) {
dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
}
}
} else {
device const half4 * x4 = (device const half4 *)x;
for (int row = 0; row < N_F16_F16; ++row) {
int r1 = rb + row;
if (r1 >= ne11) {
break;
}
device const half * y = (device const half *) (src1 + r1*nb11 + im*nb12);
device const half4 * y4 = (device const half4 *) y;
float sumf = 0;
for (int i = tiisg; i < ne00/4; i += 32) {
for (int k = 0; k < 4; ++k) sumf += (half) x4[i][k] * y4[i][k];
}
float all_sum = simd_sum(sumf);
if (tiisg == 0) {
for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (half) x[i] * y[i];
dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum;
}
}
}
}
kernel void kernel_mul_mv_f16_f32_1row(
device const char * src0,
device const char * src1,
@ -1229,6 +1302,39 @@ kernel void kernel_rope(
template [[host_name("kernel_rope_f32")]] kernel rope_t kernel_rope<float>;
template [[host_name("kernel_rope_f16")]] kernel rope_t kernel_rope<half>;
kernel void kernel_im2col_f16(
device const float * x,
device half * dst,
constant int32_t & ofs0,
constant int32_t & ofs1,
constant int32_t & IW,
constant int32_t & IH,
constant int32_t & CHW,
constant int32_t & s0,
constant int32_t & s1,
constant int32_t & p0,
constant int32_t & p1,
constant int32_t & d0,
constant int32_t & d1,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) {
const int32_t iiw = tgpig[2] * s0 + tpitg[2] * d0 - p0;
const int32_t iih = tgpig[1] * s1 + tpitg[1] * d1 - p1;
const int32_t offset_dst =
(tpitg[0] * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW +
(tgpig[0] * (ntg[1] * ntg[2]) + tpitg[1] * ntg[2] + tpitg[2]);
if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
dst[offset_dst] = 0.0f;
} else {
const int32_t offset_src = tpitg[0] * ofs0 + tgpig[0] * ofs1;
dst[offset_dst] = x[offset_src + iih * IW + iiw];
}
}
kernel void kernel_cpy_f16_f16(
device const half * src0,
device half * dst,

244
ggml-quants.c
View file

@ -14,26 +14,6 @@
//
#include <arm_neon.h>
#if !defined(__aarch64__)
inline static int32_t vaddvq_s16(int16x8_t v) {
return
(int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) +
(int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) +
(int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) +
(int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7);
}
inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) {
int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a));
int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b));
return vcombine_s16(a0, b0);
}
inline static int32_t vaddvq_s32(int32x4_t v) {
return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
}
#endif
#else
#ifdef __wasm_simd128__
@ -47,13 +27,15 @@ inline static int32_t vaddvq_s32(int32x4_t v) {
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <intrin.h>
#else
#if !defined(__riscv) && !defined(__s390__)
#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__) || defined(__SSE3__)
#if !defined(__riscv)
#include <immintrin.h>
#endif
#endif
#endif
#endif
#endif
#endif
#ifdef __riscv_v_intrinsic
#include <riscv_vector.h>
@ -61,6 +43,7 @@ inline static int32_t vaddvq_s32(int32x4_t v) {
#undef MIN
#undef MAX
#define MIN(a, b) ((a) < (b) ? (a) : (b))
#define MAX(a, b) ((a) > (b) ? (a) : (b))
@ -285,9 +268,31 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128
#endif // defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) || defined(__SSSE3__)
#if defined(__ARM_NEON)
#if !defined(__aarch64__)
// 64-bit compatibility
// vaddvq_s16
// vpaddq_s16
// vaddvq_s32
// vaddvq_f32
// vmaxvq_f32
// vcvtnq_s32_f32
inline static int32_t vaddvq_s16(int16x8_t v) {
return
(int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) +
(int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) +
(int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) +
(int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7);
}
inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) {
int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a));
int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b));
return vcombine_s16(a0, b0);
}
inline static int32_t vaddvq_s32(int32x4_t v) {
return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3);
}
@ -313,6 +318,96 @@ inline static int32x4_t vcvtnq_s32_f32(float32x4_t v) {
return res;
}
// vld1q_s16_x2
// vld1q_u8_x2
// vld1q_u8_x4
// vld1q_s8_x2
// vld1q_s8_x4
// TODO: double-check these work correctly
typedef struct ggml_int16x8x2_t {
int16x8_t val[2];
} ggml_int16x8x2_t;
inline static ggml_int16x8x2_t ggml_vld1q_s16_x2(const int16_t * ptr) {
ggml_int16x8x2_t res;
res.val[0] = vld1q_s16(ptr + 0);
res.val[1] = vld1q_s16(ptr + 8);
return res;
}
typedef struct ggml_uint8x16x2_t {
uint8x16_t val[2];
} ggml_uint8x16x2_t;
inline static ggml_uint8x16x2_t ggml_vld1q_u8_x2(const uint8_t * ptr) {
ggml_uint8x16x2_t res;
res.val[0] = vld1q_u8(ptr + 0);
res.val[1] = vld1q_u8(ptr + 16);
return res;
}
typedef struct ggml_uint8x16x4_t {
uint8x16_t val[4];
} ggml_uint8x16x4_t;
inline static ggml_uint8x16x4_t ggml_vld1q_u8_x4(const uint8_t * ptr) {
ggml_uint8x16x4_t res;
res.val[0] = vld1q_u8(ptr + 0);
res.val[1] = vld1q_u8(ptr + 16);
res.val[2] = vld1q_u8(ptr + 32);
res.val[3] = vld1q_u8(ptr + 48);
return res;
}
typedef struct ggml_int8x16x2_t {
int8x16_t val[2];
} ggml_int8x16x2_t;
inline static ggml_int8x16x2_t ggml_vld1q_s8_x2(const int8_t * ptr) {
ggml_int8x16x2_t res;
res.val[0] = vld1q_s8(ptr + 0);
res.val[1] = vld1q_s8(ptr + 16);
return res;
}
typedef struct ggml_int8x16x4_t {
int8x16_t val[4];
} ggml_int8x16x4_t;
inline static ggml_int8x16x4_t ggml_vld1q_s8_x4(const int8_t * ptr) {
ggml_int8x16x4_t res;
res.val[0] = vld1q_s8(ptr + 0);
res.val[1] = vld1q_s8(ptr + 16);
res.val[2] = vld1q_s8(ptr + 32);
res.val[3] = vld1q_s8(ptr + 48);
return res;
}
#else
#define ggml_int16x8x2_t int16x8x2_t
#define ggml_uint8x16x2_t uint8x16x2_t
#define ggml_uint8x16x4_t uint8x16x4_t
#define ggml_int8x16x2_t int8x16x2_t
#define ggml_int8x16x4_t int8x16x4_t
#define ggml_vld1q_s16_x2 vld1q_s16_x2
#define ggml_vld1q_u8_x2 vld1q_u8_x2
#define ggml_vld1q_u8_x4 vld1q_u8_x4
#define ggml_vld1q_s8_x2 vld1q_s8_x2
#define ggml_vld1q_s8_x4 vld1q_s8_x4
#endif
#endif
@ -1275,7 +1370,12 @@ static float make_qkx2_quants(int n, int nmax, const float * restrict x, const f
float max = x[0];
float sum_w = weights[0];
float sum_x = sum_w * x[0];
#ifdef HAVE_BUGGY_APPLE_LINKER
// use 'volatile' to prevent unroll and work around a bug in Apple ld64 1015.7
for (volatile int i = 1; i < n; ++i) {
#else
for (int i = 1; i < n; ++i) {
#endif
if (x[i] < min) min = x[i];
if (x[i] > max) max = x[i];
float w = weights[i];
@ -3559,7 +3659,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
const int32x4_t vzero = vdupq_n_s32(0);
#endif
int8x16x2_t q2bytes;
ggml_int8x16x2_t q2bytes;
uint8_t aux[16];
float sum = 0;
@ -3578,8 +3678,8 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
vst1q_u8(aux, scales);
const uint8x16_t mins = vshrq_n_u8(mins_and_scales, 4);
const int16x8x2_t q8sums = vld1q_s16_x2(y[i].bsums);
const int16x8x2_t mins16 = {vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))};
const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums);
const ggml_int16x8x2_t mins16 = {vreinterpretq_s16_u16(vmovl_u8(vget_low_u8(mins))), vreinterpretq_s16_u16(vmovl_u8(vget_high_u8(mins)))};
const int32x4_t s0 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[0]), vget_low_s16 (q8sums.val[0])),
vmull_s16(vget_high_s16(mins16.val[0]), vget_high_s16(q8sums.val[0])));
const int32x4_t s1 = vaddq_s32(vmull_s16(vget_low_s16 (mins16.val[1]), vget_low_s16 (q8sums.val[1])),
@ -3607,7 +3707,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
#endif
#define SHIFT_MULTIPLY_ACCUM_WITH_SCALE(shift, index)\
q8bytes = vld1q_s8_x2(q8); q8 += 32;\
q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;\
q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[0], (shift)), m3));\
q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits.val[1], (shift)), m3));\
MULTIPLY_ACCUM_WITH_SCALE((index));
@ -3615,9 +3715,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
for (int j = 0; j < QK_K/128; ++j) {
const uint8x16x2_t q2bits = vld1q_u8_x2(q2); q2 += 32;
const ggml_uint8x16x2_t q2bits = ggml_vld1q_u8_x2(q2); q2 += 32;
int8x16x2_t q8bytes = vld1q_s8_x2(q8); q8 += 32;
ggml_int8x16x2_t q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;
q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[0], m3));
q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(q2bits.val[1], m3));
MULTIPLY_ACCUM_WITH_SCALE(0);
@ -3951,7 +4051,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
const int32x4_t vzero = vdupq_n_s32(0);
#endif
int8x16x4_t q2bytes;
ggml_int8x16x4_t q2bytes;
uint32_t aux32[2];
const uint8_t * scales = (const uint8_t *)aux32;
@ -3976,7 +4076,7 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri
const uint8x16_t q2bits = vld1q_u8(q2);
const int8x16x4_t q8bytes = vld1q_s8_x4(q8);
const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8);
q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits, m3));
q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 2), m3));
@ -4240,7 +4340,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
const uint8x16_t m3 = vshlq_n_u8(m0, 3);
const int8_t m32 = 32;
int8x16x4_t q3bytes;
ggml_int8x16x4_t q3bytes;
float sum = 0;
@ -4252,9 +4352,9 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
const uint8_t * restrict qh = x[i].hmask;
const int8_t * restrict q8 = y[i].qs;
uint8x16x2_t qhbits = vld1q_u8_x2(qh);
ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh);
uint8x16x4_t q3h;
ggml_uint8x16x4_t q3h;
int32_t isum = 0;
@ -4270,9 +4370,9 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
for (int j = 0; j < QK_K/128; ++j) {
const uint8x16x2_t q3bits = vld1q_u8_x2(q3); q3 += 32;
const int8x16x4_t q8bytes_1 = vld1q_s8_x4(q8); q8 += 64;
const int8x16x4_t q8bytes_2 = vld1q_s8_x4(q8); q8 += 64;
const ggml_uint8x16x2_t q3bits = ggml_vld1q_u8_x2(q3); q3 += 32;
const ggml_int8x16x4_t q8bytes_1 = ggml_vld1q_s8_x4(q8); q8 += 64;
const ggml_int8x16x4_t q8bytes_2 = ggml_vld1q_s8_x4(q8); q8 += 64;
q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2);
q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2);
@ -4774,7 +4874,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
const uint8x16_t m3b = vdupq_n_u8(0x3);
const uint8x16_t mh = vdupq_n_u8(4);
int8x16x4_t q3bytes;
ggml_int8x16x4_t q3bytes;
uint16_t aux16[2];
int8_t * scales = (int8_t *)aux16;
@ -4783,11 +4883,11 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri
for (int i = 0; i < nb; ++i) {
uint8x16x4_t q3h;
ggml_uint8x16x4_t q3h;
const uint8x8_t hbits = vld1_u8(x[i].hmask);
const uint8x16_t q3bits = vld1q_u8(x[i].qs);
const int8x16x4_t q8bytes = vld1q_s8_x4(y[i].qs);
const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(y[i].qs);
const uint16_t a = *(const uint16_t *)x[i].scales;
aux16[0] = a & 0x0f0f;
@ -5136,8 +5236,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
const int32x4_t mzero = vdupq_n_s32(0);
#endif
int8x16x2_t q4bytes;
int8x16x2_t q8bytes;
ggml_int8x16x2_t q4bytes;
ggml_int8x16x2_t q8bytes;
float sumf = 0;
@ -5172,17 +5272,17 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
for (int j = 0; j < QK_K/64; ++j) {
const uint8x16x2_t q4bits = vld1q_u8_x2(q4); q4 += 32;
const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); q4 += 32;
#ifdef __ARM_FEATURE_DOTPROD
q8bytes = vld1q_s8_x2(q8); q8 += 32;
q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;
q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b));
q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b));
const int32x4_t p1 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]);
sumi1 += vaddvq_s32(p1) * scales[2*j+0];
q8bytes = vld1q_s8_x2(q8); q8 += 32;
q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;
q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4));
q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4));
@ -5190,7 +5290,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
sumi2 += vaddvq_s32(p2) * scales[2*j+1];
#else
q8bytes = vld1q_s8_x2(q8); q8 += 32;
q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;
q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b));
q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b));
const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
@ -5199,7 +5299,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1])));
sumi1 += vaddvq_s16(vaddq_s16(p0, p1)) * scales[2*j+0];
q8bytes = vld1q_s8_x2(q8); q8 += 32;
q8bytes = ggml_vld1q_s8_x2(q8); q8 += 32;
q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4));
q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4));
const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
@ -5514,8 +5614,8 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
float sumf = 0;
int8x16x2_t q4bytes;
int8x16x4_t q8bytes;
ggml_int8x16x2_t q4bytes;
ggml_int8x16x4_t q8bytes;
float sum_mins = 0.f;
@ -5536,10 +5636,10 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
const float d = y[i].d * (float)x[i].d[0];
const uint8x16x2_t q4bits = vld1q_u8_x2(q4);
const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4);
#ifdef __ARM_FEATURE_DOTPROD
q8bytes = vld1q_s8_x4(q8);
q8bytes = ggml_vld1q_s8_x4(q8);
q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b));
q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b));
@ -5553,7 +5653,7 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
const int32_t sumi2 = vaddvq_s32(p2) * scales[1];
#else
q8bytes = vld1q_s8_x4(q8);
q8bytes = ggml_vld1q_s8_x4(q8);
q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b));
q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b));
const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])),
@ -5787,7 +5887,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
const int32x4_t mzero = vdupq_n_s32(0);
#endif
int8x16x4_t q5bytes;
ggml_int8x16x4_t q5bytes;
float sumf = 0;
@ -5817,16 +5917,16 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
const uint8_t * restrict qh = x[i].qh;
const int8_t * restrict q8 = y[i].qs;
uint8x16x2_t qhbits = vld1q_u8_x2(qh);
ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh);
uint8x16x4_t q5h;
ggml_uint8x16x4_t q5h;
int32_t sumi = 0;
for (int j = 0; j < QK_K/64; ++j) {
const uint8x16x2_t q5bits = vld1q_u8_x2(q5); q5 += 32;
const int8x16x4_t q8bytes = vld1q_s8_x4(q8); q8 += 64;
const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5); q5 += 32;
const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64;
q5h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4);
q5h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4);
@ -6220,8 +6320,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
const int32x4_t mzero = vdupq_n_s32(0);
#endif
int8x16x4_t q5bytes;
uint8x16x4_t q5h;
ggml_int8x16x4_t q5bytes;
ggml_uint8x16x4_t q5h;
float sumf = 0;
@ -6236,8 +6336,8 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri
const uint8x8_t qhbits = vld1_u8(qh);
const uint8x16x2_t q5bits = vld1q_u8_x2(q5);
const int8x16x4_t q8bytes = vld1q_s8_x4(q8);
const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5);
const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8);
const uint8x16_t htmp = vcombine_u8(qhbits, vshr_n_u8(qhbits, 1));
q5h.val[0] = vbicq_u8(mh, vshlq_n_u8(htmp, 4));
@ -6513,8 +6613,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
const uint8x16_t mone = vdupq_n_u8(3);
int8x16x4_t q6bytes;
uint8x16x4_t q6h;
ggml_int8x16x4_t q6bytes;
ggml_uint8x16x4_t q6h;
for (int i = 0; i < nb; ++i) {
@ -6526,9 +6626,9 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
const int8_t * restrict scale = x[i].scales;
const int16x8x2_t q8sums = vld1q_s16_x2(y[i].bsums);
const ggml_int16x8x2_t q8sums = ggml_vld1q_s16_x2(y[i].bsums);
const int8x16_t scales = vld1q_s8(scale);
const int16x8x2_t q6scales = {vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))};
const ggml_int16x8x2_t q6scales = {vmovl_s8(vget_low_s8(scales)), vmovl_s8(vget_high_s8(scales))};
const int32x4_t prod = vaddq_s32(vaddq_s32(vmull_s16(vget_low_s16 (q8sums.val[0]), vget_low_s16 (q6scales.val[0])),
vmull_s16(vget_high_s16(q8sums.val[0]), vget_high_s16(q6scales.val[0]))),
@ -6540,9 +6640,9 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
for (int j = 0; j < QK_K/128; ++j) {
uint8x16x2_t qhbits = vld1q_u8_x2(qh); qh += 32;
uint8x16x4_t q6bits = vld1q_u8_x4(q6); q6 += 64;
int8x16x4_t q8bytes = vld1q_s8_x4(q8); q8 += 64;
ggml_uint8x16x2_t qhbits = ggml_vld1q_u8_x2(qh); qh += 32;
ggml_uint8x16x4_t q6bits = ggml_vld1q_u8_x4(q6); q6 += 64;
ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64;
q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4);
q6h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4);
@ -6585,7 +6685,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
scale += 2;
#endif
q8bytes = vld1q_s8_x4(q8); q8 += 64;
q8bytes = ggml_vld1q_s8_x4(q8); q8 += 64;
shifted = vshrq_n_u8(qhbits.val[0], 4);
q6h.val[0] = vshlq_n_u8(vandq_u8(mone, shifted), 4);
@ -6989,8 +7089,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
const uint8x16_t mone = vdupq_n_u8(3);
int8x16x4_t q6bytes;
uint8x16x4_t q6h;
ggml_int8x16x4_t q6bytes;
ggml_uint8x16x4_t q6h;
for (int i = 0; i < nb; ++i) {
@ -7005,8 +7105,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri
int32_t isum = 0;
uint8x16_t qhbits = vld1q_u8(qh);
uint8x16x2_t q6bits = vld1q_u8_x2(q6);
int8x16x4_t q8bytes = vld1q_s8_x4(q8);
ggml_uint8x16x2_t q6bits = ggml_vld1q_u8_x2(q6);
ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8);
q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits), 4);
uint8x16_t shifted = vshrq_n_u8(qhbits, 2);

2044
ggml.c
View file

File diff suppressed because it is too large Load diff

90
ggml.h
View file

@ -58,7 +58,8 @@
// {
// ...
//
// struct ggml_cgraph gf = ggml_build_forward(f);
// struct ggml_cgraph * gf = ggml_new_graph(ctx);
// ggml_build_forward_expand(gf, f);
//
// // set the input variable and parameter values
// ggml_set_f32(x, 2.0f);
@ -214,14 +215,13 @@
#define GGML_QNT_VERSION_FACTOR 1000 // do not change this
#define GGML_MAX_DIMS 4
#define GGML_MAX_NODES 16384
#define GGML_MAX_PARAMS 1024
#define GGML_MAX_CONTEXTS 64
#define GGML_MAX_SRC 6
#define GGML_MAX_NAME 64
#define GGML_MAX_OP_PARAMS 64
#define GGML_DEFAULT_N_THREADS 4
#define GGML_DEFAULT_GRAPH_SIZE 2048
#if UINTPTR_MAX == 0xFFFFFFFF
#define GGML_MEM_ALIGN 4
#else
@ -252,7 +252,10 @@
do { \
if (!(x)) { \
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
fflush(stderr); \
fflush(stdout); \
ggml_print_backtrace(); \
exit(1); \
} \
} while (0)
@ -407,13 +410,8 @@ extern "C" {
GGML_OP_ROPE_BACK,
GGML_OP_ALIBI,
GGML_OP_CLAMP,
GGML_OP_CONV_1D,
GGML_OP_CONV_1D_STAGE_0, // internal
GGML_OP_CONV_1D_STAGE_1, // internal
GGML_OP_CONV_TRANSPOSE_1D,
GGML_OP_CONV_2D,
GGML_OP_CONV_2D_STAGE_0, // internal
GGML_OP_CONV_2D_STAGE_1, // internal
GGML_OP_IM2COL,
GGML_OP_CONV_TRANSPOSE_2D,
GGML_OP_POOL_1D,
GGML_OP_POOL_2D,
@ -458,6 +456,7 @@ extern "C" {
GGML_UNARY_OP_GELU,
GGML_UNARY_OP_GELU_QUICK,
GGML_UNARY_OP_SILU,
GGML_UNARY_OP_LEAKY
};
enum ggml_object_type {
@ -538,37 +537,33 @@ extern "C" {
int n_threads;
// the `n_tasks` of nodes, 1:1 mapping to cgraph nodes
int n_tasks[GGML_MAX_NODES];
// abort ggml_graph_compute when true
bool (*abort_callback)(void * data);
void * abort_callback_data;
};
// next prime after GGML_MAX_NODES
// #define GGML_GRAPH_HASHTABLE_SIZE 4099
// next prime after GGML_MAX_NODES * 2 (nodes + leafs)
// #define GGML_GRAPH_HASHTABLE_SIZE 8273
// #define GGML_GRAPH_HASHTABLE_SIZE 16411
#define GGML_GRAPH_HASHTABLE_SIZE 32771
enum ggml_cgraph_eval_order {
GGML_CGRAPH_EVAL_ORDER_LEFT_TO_RIGHT = 0,
GGML_CGRAPH_EVAL_ORDER_RIGHT_TO_LEFT,
GGML_CGRAPH_EVAL_ORDER_COUNT
};
struct ggml_hash_set {
size_t size;
struct ggml_tensor ** keys;
};
// computation graph
struct ggml_cgraph {
int size;
int n_nodes;
int n_leafs;
struct ggml_tensor * nodes[GGML_MAX_NODES];
struct ggml_tensor * grads[GGML_MAX_NODES];
struct ggml_tensor * leafs[GGML_MAX_NODES];
struct ggml_tensor ** nodes;
struct ggml_tensor ** grads;
struct ggml_tensor ** leafs;
void * visited_hash_table[GGML_GRAPH_HASHTABLE_SIZE];
struct ggml_hash_set visited_hash_table;
enum ggml_cgraph_eval_order order;
@ -578,8 +573,6 @@ extern "C" {
int64_t perf_time_us;
};
static const size_t GGML_GRAPH_SIZE = sizeof(struct ggml_cgraph);
// scratch buffer
struct ggml_scratch {
size_t offs;
@ -624,6 +617,8 @@ extern "C" {
GGML_API int64_t ggml_cycles(void);
GGML_API int64_t ggml_cycles_per_ms(void);
GGML_API void ggml_print_backtrace(void);
GGML_API void ggml_numa_init(void); // call once for better performance on NUMA systems
GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node
@ -716,7 +711,7 @@ extern "C" {
// Context tensor enumeration and lookup
GGML_API struct ggml_tensor * ggml_get_first_tensor(struct ggml_context * ctx);
GGML_API struct ggml_tensor * ggml_get_next_tensor (struct ggml_context * ctx, struct ggml_tensor * tensor);
GGML_API struct ggml_tensor * ggml_get_tensor (struct ggml_context * ctx, const char * name);
GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
GGML_API struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
@ -950,6 +945,10 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_leaky(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_relu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
@ -1406,6 +1405,18 @@ extern "C" {
float min,
float max);
GGML_API struct ggml_tensor * ggml_im2col(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s0,
int s1,
int p0,
int p1,
int d0,
int d1,
bool is_2D);
GGML_API struct ggml_tensor * ggml_conv_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -1489,6 +1500,8 @@ extern "C" {
int s0, // stride
int p0); // padding
// the result will have 2*p0 padding for the first dimension
// and 2*p1 padding for the second dimension
GGML_API struct ggml_tensor * ggml_pool_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -1497,8 +1510,8 @@ extern "C" {
int k1,
int s0,
int s1,
int p0,
int p1);
float p0,
float p1);
// nearest interpolate
// used in stable-diffusion
@ -1739,19 +1752,22 @@ extern "C" {
GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);
GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
// graph allocation in a context
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx);
GGML_API struct ggml_cgraph * ggml_build_forward_ctx(struct ggml_context * ctx, struct ggml_tensor * tensor);
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
GGML_API struct ggml_cgraph * ggml_new_graph_custom (struct ggml_context * ctx, size_t size, bool grads);
GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
GGML_API struct ggml_cgraph * ggml_graph_view (struct ggml_context * ctx, struct ggml_cgraph * cgraph, int i0, int i1);
GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst);
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // zero grads
GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph);
GGML_API size_t ggml_graph_overhead(void);
GGML_API size_t ggml_graph_overhead_custom(size_t size, bool grads);
// ggml_graph_plan() has to be called before ggml_graph_compute()
// when plan.work_size > 0, caller must allocate memory for plan.work_data
GGML_API struct ggml_cplan ggml_graph_plan (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
GGML_API int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph);
// same as ggml_graph_compute() but the work data is allocated as a part of the context
// note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
@ -1760,7 +1776,7 @@ extern "C" {
GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);
GGML_API void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname);
GGML_API struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval);
GGML_API struct ggml_cgraph * ggml_graph_import(const char * fname, struct ggml_context ** ctx_data, struct ggml_context ** ctx_eval);
// print info and performance information for the graph
GGML_API void ggml_graph_print(const struct ggml_cgraph * cgraph);
@ -1823,6 +1839,8 @@ extern "C" {
struct ggml_opt_params {
enum ggml_opt_type type;
size_t graph_size;
int n_threads;
// delta-based convergence test

10
gguf-py/README.md
View file

@ -11,6 +11,16 @@ as an example for its usage.
pip install gguf
```
## API Examples/Simple Tools
[examples/writer.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/examples/writer.py) — Generates `example.gguf` in the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model.
[scripts/gguf-dump.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-dump.py) — Dumps a GGUF file's metadata to the console.
[scripts/gguf-set-metadata.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-set-metadata.py) — Allows changing simple metadata values in a GGUF file by key.
[scripts/gguf-convert-endian.py](https://github.com/ggerganov/llama.cpp/blob/master/gguf-py/scripts/gguf-convert-endian.py) — Allows converting the endianness of GGUF files.
## Development
Maintainers who participate in development of this package are advised to install it in editable mode:

40
gguf-py/examples/writer.py Executable file
View file

@ -0,0 +1,40 @@
#!/usr/bin/env python3
import sys
from pathlib import Path
import numpy as np
# Necessary to load the local gguf package
sys.path.insert(0, str(Path(__file__).parent.parent))
from gguf import GGUFWriter # noqa: E402
# Example usage:
def writer_example() -> None:
# Example usage with a file
gguf_writer = GGUFWriter("example.gguf", "llama")
gguf_writer.add_architecture()
gguf_writer.add_block_count(12)
gguf_writer.add_uint32("answer", 42) # Write a 32-bit integer
gguf_writer.add_float32("answer_in_float", 42.0) # Write a 32-bit float
gguf_writer.add_custom_alignment(64)
tensor1 = np.ones((32,), dtype=np.float32) * 100.0
tensor2 = np.ones((64,), dtype=np.float32) * 101.0
tensor3 = np.ones((96,), dtype=np.float32) * 102.0
gguf_writer.add_tensor("tensor1", tensor1)
gguf_writer.add_tensor("tensor2", tensor2)
gguf_writer.add_tensor("tensor3", tensor3)
gguf_writer.write_header_to_file()
gguf_writer.write_kv_data_to_file()
gguf_writer.write_tensors_to_file()
gguf_writer.close()
if __name__ == '__main__':
writer_example()

6
gguf-py/gguf/__init__.py
View file

@ -1 +1,5 @@
from .gguf import *
from .constants import *
from .gguf_reader import *
from .gguf_writer import *
from .tensor_mapping import *
from .vocab import *

487
gguf-py/gguf/constants.py Normal file
View file

@ -0,0 +1,487 @@
from __future__ import annotations
import sys
from enum import Enum, IntEnum, auto
from typing import Any
#
# constants
#
GGUF_MAGIC = 0x46554747 # "GGUF"
GGUF_VERSION = 3
GGUF_DEFAULT_ALIGNMENT = 32
#
# metadata keys
#
class Keys:
class General:
ARCHITECTURE = "general.architecture"
QUANTIZATION_VERSION = "general.quantization_version"
ALIGNMENT = "general.alignment"
NAME = "general.name"
AUTHOR = "general.author"
URL = "general.url"
DESCRIPTION = "general.description"
LICENSE = "general.license"
SOURCE_URL = "general.source.url"
SOURCE_HF_REPO = "general.source.huggingface.repository"
FILE_TYPE = "general.file_type"
class LLM:
CONTEXT_LENGTH = "{arch}.context_length"
EMBEDDING_LENGTH = "{arch}.embedding_length"
BLOCK_COUNT = "{arch}.block_count"
FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
class Attention:
HEAD_COUNT = "{arch}.attention.head_count"
HEAD_COUNT_KV = "{arch}.attention.head_count_kv"
MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias"
CLAMP_KQV = "{arch}.attention.clamp_kqv"
LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
class Rope:
DIMENSION_COUNT = "{arch}.rope.dimension_count"
FREQ_BASE = "{arch}.rope.freq_base"
SCALING_TYPE = "{arch}.rope.scaling.type"
SCALING_FACTOR = "{arch}.rope.scaling.factor"
SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length"
SCALING_FINETUNED = "{arch}.rope.scaling.finetuned"
class Tokenizer:
MODEL = "tokenizer.ggml.model"
LIST = "tokenizer.ggml.tokens"
TOKEN_TYPE = "tokenizer.ggml.token_type"
SCORES = "tokenizer.ggml.scores"
MERGES = "tokenizer.ggml.merges"
BOS_ID = "tokenizer.ggml.bos_token_id"
EOS_ID = "tokenizer.ggml.eos_token_id"
UNK_ID = "tokenizer.ggml.unknown_token_id"
SEP_ID = "tokenizer.ggml.seperator_token_id"
PAD_ID = "tokenizer.ggml.padding_token_id"
ADD_BOS = "tokenizer.ggml.add_bos_token"
ADD_EOS = "tokenizer.ggml.add_eos_token"
HF_JSON = "tokenizer.huggingface.json"
RWKV = "tokenizer.rwkv.world"
#
# recommended mapping of model tensor names for storage in gguf
#
class MODEL_ARCH(IntEnum):
LLAMA = auto()
FALCON = auto()
BAICHUAN = auto()
GPT2 = auto()
GPTJ = auto()
GPTNEOX = auto()
MPT = auto()
STARCODER = auto()
PERSIMMON = auto()
REFACT = auto()
BERT = auto()
BLOOM = auto()
STABLELM = auto()
class MODEL_TENSOR(IntEnum):
TOKEN_EMBD = auto()
TOKEN_EMBD_NORM = auto()
TOKEN_TYPES = auto()
POS_EMBD = auto()
OUTPUT = auto()
OUTPUT_NORM = auto()
ROPE_FREQS = auto()
ATTN_Q = auto()
ATTN_K = auto()
ATTN_V = auto()
ATTN_QKV = auto()
ATTN_OUT = auto()
ATTN_NORM = auto()
ATTN_NORM_2 = auto()
ATTN_ROT_EMBD = auto()
FFN_GATE = auto()
FFN_DOWN = auto()
FFN_UP = auto()
FFN_NORM = auto()
ATTN_Q_NORM = auto()
ATTN_K_NORM = auto()
MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.LLAMA: "llama",
MODEL_ARCH.FALCON: "falcon",
MODEL_ARCH.BAICHUAN: "baichuan",
MODEL_ARCH.GPT2: "gpt2",
MODEL_ARCH.GPTJ: "gptj",
MODEL_ARCH.GPTNEOX: "gptneox",
MODEL_ARCH.MPT: "mpt",
MODEL_ARCH.STARCODER: "starcoder",
MODEL_ARCH.PERSIMMON: "persimmon",
MODEL_ARCH.REFACT: "refact",
MODEL_ARCH.BERT: "bert",
MODEL_ARCH.BLOOM: "bloom",
MODEL_ARCH.STABLELM: "stablelm",
}
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.TOKEN_EMBD: "token_embd",
MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm",
MODEL_TENSOR.TOKEN_TYPES: "token_types",
MODEL_TENSOR.POS_EMBD: "position_embd",
MODEL_TENSOR.OUTPUT_NORM: "output_norm",
MODEL_TENSOR.OUTPUT: "output",
MODEL_TENSOR.ROPE_FREQS: "rope_freqs",
MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm",
MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2",
MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv",
MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q",
MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k",
MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v",
MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output",
MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm",
MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm",
MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm",
MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate",
MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down",
MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up",
}
MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_ARCH.LLAMA: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.GPTNEOX: [
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_UP,
],
MODEL_ARCH.FALCON: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_NORM_2,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.BAICHUAN: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.STARCODER: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.POS_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_UP,
],
MODEL_ARCH.BERT: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.TOKEN_TYPES,
MODEL_TENSOR.POS_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.MPT: [
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_UP,
],
MODEL_ARCH.GPTJ: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.PERSIMMON: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K_NORM,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
MODEL_ARCH.REFACT: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.BLOOM: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.TOKEN_EMBD_NORM,
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_UP,
],
MODEL_ARCH.STABLELM: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.GPT2: [
# TODO
],
# TODO
}
# tensors that will not be serialized
MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_ARCH.LLAMA: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
MODEL_ARCH.BAICHUAN: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
MODEL_ARCH.PERSIMMON: [
MODEL_TENSOR.ROPE_FREQS,
],
}
#
# types
#
class TokenType(IntEnum):
NORMAL = 1
UNKNOWN = 2
CONTROL = 3
USER_DEFINED = 4
UNUSED = 5
BYTE = 6
class RopeScalingType(Enum):
NONE = 'none'
LINEAR = 'linear'
YARN = 'yarn'
class GGMLQuantizationType(IntEnum):
F32 = 0
F16 = 1
Q4_0 = 2
Q4_1 = 3
Q5_0 = 6
Q5_1 = 7
Q8_0 = 8
Q8_1 = 9
Q2_K = 10
Q3_K = 11
Q4_K = 12
Q5_K = 13
Q6_K = 14
Q8_K = 15
class GGUFEndian(IntEnum):
LITTLE = 0
BIG = 1
class GGUFValueType(IntEnum):
UINT8 = 0
INT8 = 1
UINT16 = 2
INT16 = 3
UINT32 = 4
INT32 = 5
FLOAT32 = 6
BOOL = 7
STRING = 8
ARRAY = 9
UINT64 = 10
INT64 = 11
FLOAT64 = 12
@staticmethod
def get_type(val: Any) -> GGUFValueType:
if isinstance(val, (str, bytes, bytearray)):
return GGUFValueType.STRING
elif isinstance(val, list):
return GGUFValueType.ARRAY
elif isinstance(val, float):
return GGUFValueType.FLOAT32
elif isinstance(val, bool):
return GGUFValueType.BOOL
elif isinstance(val, int):
return GGUFValueType.INT32
# TODO: need help with 64-bit types in Python
else:
print("Unknown type:", type(val))
sys.exit()
# Note: Does not support GGML_QKK_64
QK_K = 256
# Items here are (block size, type size)
GGML_QUANT_SIZES = {
GGMLQuantizationType.F32: (1, 4),
GGMLQuantizationType.F16: (1, 2),
GGMLQuantizationType.Q4_0: (32, 2 + 16),
GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16),
GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16),
GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16),
GGMLQuantizationType.Q8_0: (32, 2 + 32),
GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32),
GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4),
GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12),
GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12),
GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12),
GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16),
GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8),
}
# Aliases for backward compatibility.
# general
KEY_GENERAL_ARCHITECTURE = Keys.General.ARCHITECTURE
KEY_GENERAL_QUANTIZATION_VERSION = Keys.General.QUANTIZATION_VERSION
KEY_GENERAL_ALIGNMENT = Keys.General.ALIGNMENT
KEY_GENERAL_NAME = Keys.General.NAME
KEY_GENERAL_AUTHOR = Keys.General.AUTHOR
KEY_GENERAL_URL = Keys.General.URL
KEY_GENERAL_DESCRIPTION = Keys.General.DESCRIPTION
KEY_GENERAL_LICENSE = Keys.General.LICENSE
KEY_GENERAL_SOURCE_URL = Keys.General.SOURCE_URL
KEY_GENERAL_SOURCE_HF_REPO = Keys.General.SOURCE_HF_REPO
KEY_GENERAL_FILE_TYPE = Keys.General.FILE_TYPE
# LLM
KEY_CONTEXT_LENGTH = Keys.LLM.CONTEXT_LENGTH
KEY_EMBEDDING_LENGTH = Keys.LLM.EMBEDDING_LENGTH
KEY_BLOCK_COUNT = Keys.LLM.BLOCK_COUNT
KEY_FEED_FORWARD_LENGTH = Keys.LLM.FEED_FORWARD_LENGTH
KEY_USE_PARALLEL_RESIDUAL = Keys.LLM.USE_PARALLEL_RESIDUAL
KEY_TENSOR_DATA_LAYOUT = Keys.LLM.TENSOR_DATA_LAYOUT
# attention
KEY_ATTENTION_HEAD_COUNT = Keys.Attention.HEAD_COUNT
KEY_ATTENTION_HEAD_COUNT_KV = Keys.Attention.HEAD_COUNT_KV
KEY_ATTENTION_MAX_ALIBI_BIAS = Keys.Attention.MAX_ALIBI_BIAS
KEY_ATTENTION_CLAMP_KQV = Keys.Attention.CLAMP_KQV
KEY_ATTENTION_LAYERNORM_EPS = Keys.Attention.LAYERNORM_EPS
KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS
# RoPE
KEY_ROPE_DIMENSION_COUNT = Keys.Rope.DIMENSION_COUNT
KEY_ROPE_FREQ_BASE = Keys.Rope.FREQ_BASE
KEY_ROPE_SCALING_TYPE = Keys.Rope.SCALING_TYPE
KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR
KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN
KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED
# tokenization
KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL
KEY_TOKENIZER_LIST = Keys.Tokenizer.LIST
KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE
KEY_TOKENIZER_SCORES = Keys.Tokenizer.SCORES
KEY_TOKENIZER_MERGES = Keys.Tokenizer.MERGES
KEY_TOKENIZER_BOS_ID = Keys.Tokenizer.BOS_ID
KEY_TOKENIZER_EOS_ID = Keys.Tokenizer.EOS_ID
KEY_TOKENIZER_UNK_ID = Keys.Tokenizer.UNK_ID
KEY_TOKENIZER_SEP_ID = Keys.Tokenizer.SEP_ID
KEY_TOKENIZER_PAD_ID = Keys.Tokenizer.PAD_ID
KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON
KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV

1149
gguf-py/gguf/gguf.py
View file

File diff suppressed because it is too large Load diff

264
gguf-py/gguf/gguf_reader.py Normal file
View file

@ -0,0 +1,264 @@
#
# GGUF file reading/modification support. For API usage information,
# please see the files scripts/ for some fairly simple examples.
#
from __future__ import annotations
import os
from collections import OrderedDict
from typing import Any, Literal, NamedTuple, TypeVar, Union
import numpy as np
import numpy.typing as npt
if __name__ == "__main__":
import sys
from pathlib import Path
# Allow running file in package as a script.
sys.path.insert(0, str(Path(__file__).parent.parent))
from gguf.constants import (
GGML_QUANT_SIZES,
GGUF_DEFAULT_ALIGNMENT,
GGUF_MAGIC,
GGUF_VERSION,
GGMLQuantizationType,
GGUFValueType,
)
READER_SUPPORTED_VERSIONS = [2, GGUF_VERSION]
class ReaderField(NamedTuple):
# Offset to start of this field.
offset: int
# Name of the field (not necessarily from file data).
name: str
# Data parts. Some types have multiple components, such as strings
# that consist of a length followed by the string data.
parts: list[npt.NDArray[Any]] = []
# Indexes into parts that we can call the actual data. For example
# an array of strings will be populated with indexes to the actual
# string data.
data: list[int] = [-1]
types: list[GGUFValueType] = []
class ReaderTensor(NamedTuple):
name: str
tensor_type: GGMLQuantizationType
shape: npt.NDArray[np.uint32]
n_elements: int
n_bytes: int
data_offset: int
data: npt.NDArray[Any]
field: ReaderField
class GGUFReader:
# I - same as host, S - swapped
byte_order: Literal['I' | 'S'] = 'I'
alignment: int = GGUF_DEFAULT_ALIGNMENT
# Note: Internal helper, API may change.
gguf_scalar_to_np: dict[GGUFValueType, type[np.generic]] = {
GGUFValueType.UINT8: np.uint8,
GGUFValueType.INT8: np.int8,
GGUFValueType.UINT16: np.uint16,
GGUFValueType.INT16: np.int16,
GGUFValueType.UINT32: np.uint32,
GGUFValueType.INT32: np.int32,
GGUFValueType.FLOAT32: np.float32,
GGUFValueType.UINT64: np.uint64,
GGUFValueType.INT64: np.int64,
GGUFValueType.FLOAT64: np.float64,
GGUFValueType.BOOL: np.bool_,
}
def __init__(self, path: os.PathLike[str] | str, mode: Literal['r' | 'r+' | 'c'] = 'r'):
self.data = np.memmap(path, mode = mode)
offs = 0
if self._get(offs, np.uint32, override_order = '<')[0] != GGUF_MAGIC:
raise ValueError('GGUF magic invalid')
offs += 4
temp_version = self._get(offs, np.uint32)
if temp_version[0] & 65535 == 0:
# If we get 0 here that means it's (probably) a GGUF file created for
# the opposite byte order of the machine this script is running on.
self.byte_order = 'S'
temp_version = temp_version.newbyteorder(self.byte_order)
version = temp_version[0]
if version not in READER_SUPPORTED_VERSIONS:
raise ValueError(f'Sorry, file appears to be version {version} which we cannot handle')
self.fields: OrderedDict[str, ReaderField] = OrderedDict()
self.tensors: list[ReaderTensor] = []
offs += self._push_field(ReaderField(offs, 'GGUF.version', [temp_version], [0], [GGUFValueType.UINT32]))
temp_counts = self._get(offs, np.uint64, 2)
offs += self._push_field(ReaderField(offs, 'GGUF.tensor_count', [temp_counts[:1]], [0], [GGUFValueType.UINT64]))
offs += self._push_field(ReaderField(offs, 'GGUF.kv_count', [temp_counts[1:]], [0], [GGUFValueType.UINT64]))
tensor_count, kv_count = temp_counts
offs = self._build_fields(offs, kv_count)
offs, tensors_fields = self._build_tensors_fields(offs, tensor_count)
new_align = self.fields.get('general.alignment')
if new_align is not None:
if new_align.types != [GGUFValueType.UINT64]:
raise ValueError('Bad type for general.alignment field')
self.alignment = new_align.parts[-1][0]
padding = offs % self.alignment
if padding != 0:
offs += self.alignment - padding
self._build_tensors(offs, tensors_fields)
_DT = TypeVar('_DT', bound = npt.DTypeLike)
# Fetch a key/value metadata field by key.
def get_field(self, key: str) -> Union[ReaderField, None]:
return self.fields.get(key, None)
# Fetch a tensor from the list by index.
def get_tensor(self, idx: int) -> ReaderTensor:
return self.tensors[idx]
def _get(
self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I' | 'S' | '<'] = None,
) -> npt.NDArray[Any]:
count = int(count)
itemsize = int(np.empty([], dtype = dtype).itemsize)
end_offs = offset + itemsize * count
return (
self.data[offset:end_offs]
.view(dtype = dtype)[:count]
.newbyteorder(override_order or self.byte_order)
)
def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int:
if field.name in self.fields:
raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}')
self.fields[field.name] = field
return 0 if skip_sum else sum(int(part.nbytes) for part in field.parts)
def _get_str(self, offset: int) -> tuple[npt.NDArray[np.uint64], npt.NDArray[np.uint8]]:
slen = self._get(offset, np.uint64)
return slen, self._get(offset + 8, np.uint8, slen[0])
def _get_field_parts(
self, orig_offs: int, raw_type: int,
) -> tuple[int, list[npt.NDArray[Any]], list[int], list[GGUFValueType]]:
offs = orig_offs
types: list[GGUFValueType] = []
gtype = GGUFValueType(raw_type)
types.append(gtype)
# Handle strings.
if gtype == GGUFValueType.STRING:
sparts: list[npt.NDArray[Any]] = list(self._get_str(offs))
size = sum(int(part.nbytes) for part in sparts)
return size, sparts, [1], types
# Check if it's a simple scalar type.
nptype = self.gguf_scalar_to_np.get(gtype)
if nptype is not None:
val = self._get(offs, nptype)
return int(val.nbytes), [val], [0], types
# Handle arrays.
if gtype == GGUFValueType.ARRAY:
raw_itype = self._get(offs, np.uint32)
offs += int(raw_itype.nbytes)
alen = self._get(offs, np.uint64)
offs += int(alen.nbytes)
aparts: list[npt.NDArray[Any]] = [raw_itype, alen]
data_idxs: list[int] = []
for idx in range(alen[0]):
curr_size, curr_parts, curr_idxs, curr_types = self._get_field_parts(offs, raw_itype[0])
if idx == 0:
types += curr_types
idxs_offs = len(aparts)
aparts += curr_parts
data_idxs += (idx + idxs_offs for idx in curr_idxs)
offs += curr_size
return offs - orig_offs, aparts, data_idxs, types
# We can't deal with this one.
raise ValueError('Unknown/unhandled field type {gtype}')
def _get_tensor(self, orig_offs: int) -> ReaderField:
offs = orig_offs
name_len, name_data = self._get_str(offs)
offs += int(name_len.nbytes + name_data.nbytes)
n_dims = self._get(offs, np.uint32)
offs += int(n_dims.nbytes)
dims = self._get(offs, np.uint64, n_dims[0])
offs += int(dims.nbytes)
raw_dtype = self._get(offs, np.uint32)
offs += int(raw_dtype.nbytes)
offset_tensor = self._get(offs, np.uint64)
offs += int(offset_tensor.nbytes)
return ReaderField(
orig_offs,
str(bytes(name_data), encoding = 'utf-8'),
[name_len, name_data, n_dims, dims, raw_dtype, offset_tensor],
[1, 3, 4, 5],
)
def _build_fields(self, offs: int, count: int) -> int:
for _ in range(count):
orig_offs = offs
kv_klen, kv_kdata = self._get_str(offs)
offs += int(kv_klen.nbytes + kv_kdata.nbytes)
raw_kv_type = self._get(offs, np.uint32)
offs += int(raw_kv_type.nbytes)
parts: list[npt.NDArray[Any]] = [kv_klen, kv_kdata, raw_kv_type]
idxs_offs = len(parts)
field_size, field_parts, field_idxs, field_types = self._get_field_parts(offs, raw_kv_type[0])
parts += field_parts
self._push_field(ReaderField(
orig_offs,
str(bytes(kv_kdata), encoding = 'utf-8'),
parts,
[idx + idxs_offs for idx in field_idxs],
field_types,
), skip_sum = True)
offs += field_size
return offs
def _build_tensors_fields(self, offs: int, count: int) -> tuple[int, list[ReaderField]]:
tensor_fields = []
for _ in range(count):
field = self._get_tensor(offs)
offs += sum(int(part.nbytes) for part in field.parts)
tensor_fields.append(field)
return offs, tensor_fields
def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None:
tensors = []
for field in fields:
_name_len, name_data, _n_dims, dims, raw_dtype, offset_tensor = field.parts
ggml_type = GGMLQuantizationType(raw_dtype[0])
n_elems = np.prod(dims)
block_size, type_size = GGML_QUANT_SIZES[ggml_type]
n_bytes = n_elems * type_size // block_size
data_offs = int(start_offs + offset_tensor[0])
item_type: npt.DTypeLike
if ggml_type == GGMLQuantizationType.F32:
item_count = n_elems
item_type = np.float32
elif ggml_type == GGMLQuantizationType.F16:
item_count = n_elems
item_type = np.float16
else:
item_count = n_bytes
item_type = np.uint8
tensors.append(ReaderTensor(
name = str(bytes(name_data), encoding = 'utf-8'),
tensor_type = ggml_type,
shape = dims,
n_elements = n_elems,
n_bytes = n_bytes,
data_offset = data_offs,
data = self._get(data_offs, item_type, item_count),
field = field,
))
self.tensors = tensors

409
gguf-py/gguf/gguf_writer.py Normal file
View file

@ -0,0 +1,409 @@
from __future__ import annotations
import os
import shutil
import struct
import tempfile
from enum import Enum, auto
from io import BufferedWriter
from typing import IO, Any, Sequence
import numpy as np
from .constants import (
GGUF_DEFAULT_ALIGNMENT,
GGUF_MAGIC,
GGUF_VERSION,
GGMLQuantizationType,
GGUFEndian,
GGUFValueType,
Keys,
RopeScalingType,
TokenType,
)
class WriterState(Enum):
EMPTY = auto()
HEADER = auto()
KV_DATA = auto()
TI_DATA = auto()
class GGUFWriter:
fout: BufferedWriter
temp_file: tempfile.SpooledTemporaryFile[bytes] | None
tensors: list[np.ndarray[Any, Any]]
_simple_value_packing = {
GGUFValueType.UINT8: "B",
GGUFValueType.INT8: "b",
GGUFValueType.UINT16: "H",
GGUFValueType.INT16: "h",
GGUFValueType.UINT32: "I",
GGUFValueType.INT32: "i",
GGUFValueType.FLOAT32: "f",
GGUFValueType.UINT64: "Q",
GGUFValueType.INT64: "q",
GGUFValueType.FLOAT64: "d",
GGUFValueType.BOOL: "?",
}
def __init__(
self, path: os.PathLike[str] | str, arch: str, use_temp_file: bool = True,
endianess: GGUFEndian = GGUFEndian.LITTLE,
):
self.fout = open(path, "wb")
self.arch = arch
self.endianess = endianess
self.offset_tensor = 0
self.data_alignment = GGUF_DEFAULT_ALIGNMENT
self.kv_data = bytearray()
self.kv_data_count = 0
self.ti_data = bytearray()
self.ti_data_count = 0
self.use_temp_file = use_temp_file
self.temp_file = None
self.tensors = []
print("gguf: This GGUF file is for {0} Endian only".format(
"Big" if self.endianess == GGUFEndian.BIG else "Little",
))
self.state = WriterState.EMPTY
self.add_architecture()
def write_header_to_file(self) -> None:
if self.state is not WriterState.EMPTY:
raise ValueError(f'Expected output file to be empty, got {self.state}')
self._write_packed("<I", GGUF_MAGIC, skip_pack_prefix = True)
self._write_packed("I", GGUF_VERSION)
self._write_packed("Q", self.ti_data_count)
self._write_packed("Q", self.kv_data_count)
self.flush()
self.state = WriterState.HEADER
def write_kv_data_to_file(self) -> None:
if self.state is not WriterState.HEADER:
raise ValueError(f'Expected output file to contain the header, got {self.state}')
self.fout.write(self.kv_data)
self.flush()
self.state = WriterState.KV_DATA
def write_ti_data_to_file(self) -> None:
if self.state is not WriterState.KV_DATA:
raise ValueError(f'Expected output file to contain KV data, got {self.state}')
self.fout.write(self.ti_data)
self.flush()
self.state = WriterState.TI_DATA
def add_key(self, key: str) -> None:
self.add_val(key, GGUFValueType.STRING, add_vtype=False)
def add_uint8(self, key: str, val: int) -> None:
self.add_key(key)
self.add_val(val, GGUFValueType.UINT8)
def add_int8(self, key: str, val: int) -> None:
self.add_key(key)
self.add_val(val, GGUFValueType.INT8)
def add_uint16(self, key: str, val: int) -> None:
self.add_key(key)
self.add_val(val, GGUFValueType.UINT16)
def add_int16(self, key: str, val: int) -> None:
self.add_key(key)
self.add_val(val, GGUFValueType.INT16)
def add_uint32(self, key: str, val: int) -> None:
self.add_key(key)
self.add_val(val, GGUFValueType.UINT32)
def add_int32(self, key: str, val: int) -> None:
self.add_key(key)
self.add_val(val, GGUFValueType.INT32)
def add_float32(self, key: str, val: float) -> None:
self.add_key(key)
self.add_val(val, GGUFValueType.FLOAT32)
def add_uint64(self, key: str, val: int) -> None:
self.add_key(key)
self.add_val(val, GGUFValueType.UINT64)
def add_int64(self, key: str, val: int) -> None:
self.add_key(key)
self.add_val(val, GGUFValueType.INT64)
def add_float64(self, key: str, val: float) -> None:
self.add_key(key)
self.add_val(val, GGUFValueType.FLOAT64)
def add_bool(self, key: str, val: bool) -> None:
self.add_key(key)
self.add_val(val, GGUFValueType.BOOL)
def add_string(self, key: str, val: str) -> None:
if not val:
return
self.add_key(key)
self.add_val(val, GGUFValueType.STRING)
def add_array(self, key: str, val: Sequence[Any]) -> None:
if not isinstance(val, Sequence):
raise ValueError("Value must be a sequence for array type")
self.add_key(key)
self.add_val(val, GGUFValueType.ARRAY)
def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True) -> None:
if vtype is None:
vtype = GGUFValueType.get_type(val)
if add_vtype:
self.kv_data += self._pack("I", vtype)
self.kv_data_count += 1
pack_fmt = self._simple_value_packing.get(vtype)
if pack_fmt is not None:
self.kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL)
elif vtype == GGUFValueType.STRING:
encoded_val = val.encode("utf8") if isinstance(val, str) else val
self.kv_data += self._pack("Q", len(encoded_val))
self.kv_data += encoded_val
elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val:
ltype = GGUFValueType.get_type(val[0])
if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]):
raise ValueError("All items in a GGUF array should be of the same type")
self.kv_data += self._pack("I", ltype)
self.kv_data += self._pack("Q", len(val))
for item in val:
self.add_val(item, add_vtype=False)
else:
raise ValueError("Invalid GGUF metadata value type or value")
@staticmethod
def ggml_pad(x: int, n: int) -> int:
return ((x + n - 1) // n) * n
def add_tensor_info(
self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32],
tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None,
) -> None:
if self.state is not WriterState.EMPTY:
raise ValueError(f'Expected output file to be empty, got {self.state}')
if raw_dtype is None and tensor_dtype not in (np.float32, np.float16):
raise ValueError("Only F32 and F16 tensors are supported for now")
encoded_name = name.encode("utf8")
self.ti_data += self._pack("Q", len(encoded_name))
self.ti_data += encoded_name
n_dims = len(tensor_shape)
self.ti_data += self._pack("I", n_dims)
for i in range(n_dims):
self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i])
if raw_dtype is None:
dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
else:
dtype = raw_dtype
self.ti_data += self._pack("I", dtype)
self.ti_data += self._pack("Q", self.offset_tensor)
self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
self.ti_data_count += 1
def add_tensor(
self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None,
raw_dtype: GGMLQuantizationType | None = None,
) -> None:
if self.endianess == GGUFEndian.BIG:
tensor.byteswap(inplace=True)
if self.use_temp_file and self.temp_file is None:
fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
fp.seek(0)
self.temp_file = fp
shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape
self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype)
if self.temp_file is None:
self.tensors.append(tensor)
return
tensor.tofile(self.temp_file)
self.write_padding(self.temp_file, tensor.nbytes)
def write_padding(self, fp: IO[bytes], n: int, align: int | None = None) -> None:
pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n
if pad != 0:
fp.write(bytes([0] * pad))
def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None:
if self.state is not WriterState.TI_DATA:
raise ValueError(f'Expected output file to contain tensor info, got {self.state}')
if self.endianess == GGUFEndian.BIG:
tensor.byteswap(inplace=True)
self.write_padding(self.fout, self.fout.tell())
tensor.tofile(self.fout)
self.write_padding(self.fout, tensor.nbytes)
def write_tensors_to_file(self) -> None:
self.write_ti_data_to_file()
self.write_padding(self.fout, self.fout.tell())
if self.temp_file is None:
while True:
try:
tensor = self.tensors.pop(0)
except IndexError:
break
tensor.tofile(self.fout)
self.write_padding(self.fout, tensor.nbytes)
return
self.temp_file.seek(0)
shutil.copyfileobj(self.temp_file, self.fout)
self.flush()
self.temp_file.close()
def flush(self) -> None:
self.fout.flush()
def close(self) -> None:
self.fout.close()
def add_architecture(self) -> None:
self.add_string(Keys.General.ARCHITECTURE, self.arch)
def add_author(self, author: str) -> None:
self.add_string(Keys.General.AUTHOR, author)
def add_tensor_data_layout(self, layout: str) -> None:
self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
def add_url(self, url: str) -> None:
self.add_string(Keys.General.URL, url)
def add_description(self, description: str) -> None:
self.add_string(Keys.General.DESCRIPTION, description)
def add_source_url(self, url: str) -> None:
self.add_string(Keys.General.SOURCE_URL, url)
def add_source_hf_repo(self, repo: str) -> None:
self.add_string(Keys.General.SOURCE_HF_REPO, repo)
def add_file_type(self, ftype: int) -> None:
self.add_uint32(Keys.General.FILE_TYPE, ftype)
def add_name(self, name: str) -> None:
self.add_string(Keys.General.NAME, name)
def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None:
self.add_uint32(
Keys.General.QUANTIZATION_VERSION, quantization_version)
def add_custom_alignment(self, alignment: int) -> None:
self.data_alignment = alignment
self.add_uint32(Keys.General.ALIGNMENT, alignment)
def add_context_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length)
def add_embedding_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length)
def add_block_count(self, length: int) -> None:
self.add_uint32(Keys.LLM.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_parallel_residual(self, use: bool) -> None:
self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
def add_head_count(self, count: int) -> None:
self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count)
def add_head_count_kv(self, count: int) -> None:
self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count)
def add_max_alibi_bias(self, bias: float) -> None:
self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias)
def add_clamp_kqv(self, value: float) -> None:
self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value)
def add_layer_norm_eps(self, value: float) -> None:
self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value)
def add_layer_norm_rms_eps(self, value: float) -> None:
self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value)
def add_rope_dimension_count(self, count: int) -> None:
self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count)
def add_rope_freq_base(self, value: float) -> None:
self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value)
def add_rope_scaling_type(self, value: RopeScalingType) -> None:
self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value)
def add_rope_scaling_factor(self, value: float) -> None:
self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value)
def add_rope_scaling_orig_ctx_len(self, value: int) -> None:
self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value)
def add_rope_scaling_finetuned(self, value: bool) -> None:
self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value)
def add_tokenizer_model(self, model: str) -> None:
self.add_string(Keys.Tokenizer.MODEL, model)
def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
self.add_array(Keys.Tokenizer.LIST, tokens)
def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None:
self.add_array(Keys.Tokenizer.MERGES, merges)
def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None:
self.add_array(Keys.Tokenizer.TOKEN_TYPE, types)
def add_token_scores(self, scores: Sequence[float]) -> None:
self.add_array(Keys.Tokenizer.SCORES, scores)
def add_bos_token_id(self, id: int) -> None:
self.add_uint32(Keys.Tokenizer.BOS_ID, id)
def add_eos_token_id(self, id: int) -> None:
self.add_uint32(Keys.Tokenizer.EOS_ID, id)
def add_unk_token_id(self, id: int) -> None:
self.add_uint32(Keys.Tokenizer.UNK_ID, id)
def add_sep_token_id(self, id: int) -> None:
self.add_uint32(Keys.Tokenizer.SEP_ID, id)
def add_pad_token_id(self, id: int) -> None:
self.add_uint32(Keys.Tokenizer.PAD_ID, id)
def add_add_bos_token(self, value: bool) -> None:
self.add_bool(Keys.Tokenizer.ADD_BOS, value)
def add_add_eos_token(self, value: bool) -> None:
self.add_bool(Keys.Tokenizer.ADD_EOS, value)
def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes:
pack_prefix = ''
if not skip_pack_prefix:
pack_prefix = '<' if self.endianess == GGUFEndian.LITTLE else '>'
return struct.pack(f'{pack_prefix}{fmt}', value)
def _write_packed(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> None:
self.fout.write(self._pack(fmt, value, skip_pack_prefix))

257
gguf-py/gguf/tensor_mapping.py Normal file
View file

@ -0,0 +1,257 @@
from __future__ import annotations
from typing import Sequence
from .constants import MODEL_ARCH, MODEL_TENSOR, MODEL_TENSORS, TENSOR_NAMES
class TensorNameMap:
mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
# Token embeddings
MODEL_TENSOR.TOKEN_EMBD: (
"gpt_neox.embed_in", # gptneox
"transformer.wte", # gpt2 gpt-j mpt refact
"transformer.word_embeddings", # falcon
"word_embeddings", # bloom
"model.embed_tokens", # llama-hf
"tok_embeddings", # llama-pth
"embeddings.word_embeddings", # bert
"language_model.embedding.word_embeddings", # persimmon
),
# Token type embeddings
MODEL_TENSOR.TOKEN_TYPES: (
"embeddings.token_type_embeddings", # bert
),
# Normalization of token embeddings
MODEL_TENSOR.TOKEN_EMBD_NORM: (
"word_embeddings_layernorm", # bloom
),
# Position embeddings
MODEL_TENSOR.POS_EMBD: (
"transformer.wpe", # gpt2
"embeddings.position_embeddings", # bert
),
# Output
MODEL_TENSOR.OUTPUT: (
"embed_out", # gptneox
"lm_head", # gpt2 mpt falcon llama-hf baichuan
"output", # llama-pth bloom
"word_embeddings_for_head", # persimmon
),
# Output norm
MODEL_TENSOR.OUTPUT_NORM: (
"gpt_neox.final_layer_norm", # gptneox
"transformer.ln_f", # gpt2 gpt-j falcon
"model.norm", # llama-hf baichuan
"norm", # llama-pth
"embeddings.LayerNorm", # bert
"transformer.norm_f", # mpt
"ln_f", # refact bloom
"language_model.encoder.final_layernorm", # persimmon
),
# Rope frequencies
MODEL_TENSOR.ROPE_FREQS: (
"rope.freqs", # llama-pth
),
}
block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
# Attention norm
MODEL_TENSOR.ATTN_NORM: (
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact
"transformer.blocks.{bid}.norm_1", # mpt
"transformer.h.{bid}.input_layernorm", # falcon7b
"h.{bid}.input_layernorm", # bloom
"transformer.h.{bid}.ln_mlp", # falcon40b
"model.layers.{bid}.input_layernorm", # llama-hf
"layers.{bid}.attention_norm", # llama-pth
"encoder.layer.{bid}.attention.output.LayerNorm", # bert
"language_model.encoder.layers.{bid}.input_layernorm", # persimmon
"model.layers.{bid}.ln1", # yi
),
# Attention norm 2
MODEL_TENSOR.ATTN_NORM_2: (
"transformer.h.{bid}.ln_attn", # falcon40b
),
# Attention query-key-value
MODEL_TENSOR.ATTN_QKV: (
"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
"transformer.h.{bid}.attn.c_attn", # gpt2
"transformer.blocks.{bid}.attn.Wqkv", # mpt
"transformer.h.{bid}.self_attention.query_key_value", # falcon
"h.{bid}.self_attention.query_key_value", # bloom
"language_model.encoder.layers.{bid}.self_attention.query_key_value", # persimmon
),
# Attention query
MODEL_TENSOR.ATTN_Q: (
"model.layers.{bid}.self_attn.q_proj", # llama-hf
"layers.{bid}.attention.wq", # llama-pth
"encoder.layer.{bid}.attention.self.query", # bert
"transformer.h.{bid}.attn.q_proj", # gpt-j
),
# Attention key
MODEL_TENSOR.ATTN_K: (
"model.layers.{bid}.self_attn.k_proj", # llama-hf
"layers.{bid}.attention.wk", # llama-pth
"encoder.layer.{bid}.attention.self.key", # bert
"transformer.h.{bid}.attn.k_proj", # gpt-j
),
# Attention value
MODEL_TENSOR.ATTN_V: (
"model.layers.{bid}.self_attn.v_proj", # llama-hf
"layers.{bid}.attention.wv", # llama-pth
"encoder.layer.{bid}.attention.self.value", # bert
"transformer.h.{bid}.attn.v_proj", # gpt-j
),
# Attention output
MODEL_TENSOR.ATTN_OUT: (
"gpt_neox.layers.{bid}.attention.dense", # gptneox
"transformer.h.{bid}.attn.c_proj", # gpt2 refact
"transformer.blocks.{bid}.attn.out_proj", # mpt
"transformer.h.{bid}.self_attention.dense", # falcon
"h.{bid}.self_attention.dense", # bloom
"model.layers.{bid}.self_attn.o_proj", # llama-hf
"layers.{bid}.attention.wo", # llama-pth
"encoder.layer.{bid}.attention.output.dense", # bert
"transformer.h.{bid}.attn.out_proj", # gpt-j
"language_model.encoder.layers.{bid}.self_attention.dense", # persimmon
),
# Rotary embeddings
MODEL_TENSOR.ATTN_ROT_EMBD: (
"model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf
"layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth
),
# Feed-forward norm
MODEL_TENSOR.FFN_NORM: (
"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
"transformer.h.{bid}.ln_2", # gpt2 refact
"h.{bid}.post_attention_layernorm", # bloom
"transformer.blocks.{bid}.norm_2", # mpt
"model.layers.{bid}.post_attention_layernorm", # llama-hf
"layers.{bid}.ffn_norm", # llama-pth
"encoder.layer.{bid}.output.LayerNorm", # bert
"language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon
"model.layers.{bid}.ln2", # yi
),
# 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.blocks.{bid}.ffn.up_proj", # mpt
"transformer.h.{bid}.mlp.dense_h_to_4h", # falcon
"h.{bid}.mlp.dense_h_to_4h", # bloom
"model.layers.{bid}.mlp.up_proj", # llama-hf refact
"layers.{bid}.feed_forward.w3", # llama-pth
"encoder.layer.{bid}.intermediate.dense", # bert
"transformer.h.{bid}.mlp.fc_in", # gpt-j
"language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
),
# Feed-forward gate
MODEL_TENSOR.FFN_GATE: (
"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
"layers.{bid}.feed_forward.w1", # llama-pth
),
# 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
"transformer.blocks.{bid}.ffn.down_proj", # mpt
"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
"h.{bid}.mlp.dense_4h_to_h", # bloom
"model.layers.{bid}.mlp.down_proj", # llama-hf
"layers.{bid}.feed_forward.w2", # llama-pth
"encoder.layer.{bid}.output.dense", # bert
"transformer.h.{bid}.mlp.fc_out", # gpt-j
"language_model.encoder.layers.{bid}.mlp.dense_4h_to_h", # persimmon
),
MODEL_TENSOR.ATTN_Q_NORM: (
"language_model.encoder.layers.{bid}.self_attention.q_layernorm",
),
MODEL_TENSOR.ATTN_K_NORM: (
"language_model.encoder.layers.{bid}.self_attention.k_layernorm",
),
MODEL_TENSOR.ROPE_FREQS: (
"language_model.encoder.layers.{bid}.self_attention.rotary_emb.inv_freq", # persimmon
),
}
mapping: dict[str, tuple[MODEL_TENSOR, str]]
def __init__(self, arch: MODEL_ARCH, n_blocks: int):
self.mapping = {}
for tensor, keys in self.mappings_cfg.items():
if tensor not in MODEL_TENSORS[arch]:
continue
tensor_name = TENSOR_NAMES[tensor]
self.mapping[tensor_name] = (tensor, tensor_name)
for key in keys:
self.mapping[key] = (tensor, tensor_name)
for bid in range(n_blocks):
for tensor, keys in self.block_mappings_cfg.items():
if tensor not in MODEL_TENSORS[arch]:
continue
tensor_name = TENSOR_NAMES[tensor].format(bid = bid)
self.mapping[tensor_name] = (tensor, tensor_name)
for key in keys:
key = key.format(bid = bid)
self.mapping[key] = (tensor, tensor_name)
def get_type_and_name(self, key: str, try_suffixes: Sequence[str] = ()) -> tuple[MODEL_TENSOR, str] | None:
result = self.mapping.get(key)
if result is not None:
return result
for suffix in try_suffixes:
if key.endswith(suffix):
result = self.mapping.get(key[:-len(suffix)])
if result is not None:
return result[0], result[1] + suffix
return None
def get_name(self, key: str, try_suffixes: Sequence[str] = ()) -> str | None:
result = self.get_type_and_name(key, try_suffixes = try_suffixes)
if result is None:
return None
return result[1]
def get_type(self, key: str, try_suffixes: Sequence[str] = ()) -> MODEL_TENSOR | None:
result = self.get_type_and_name(key, try_suffixes = try_suffixes)
if result is None:
return None
return result[0]
def __getitem__(self, key: str) -> str:
try:
return self.mapping[key][1]
except KeyError:
raise KeyError(key)
def __contains__(self, key: str) -> bool:
return key in self.mapping
def __repr__(self) -> str:
return repr(self.mapping)
def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap:
return TensorNameMap(arch, n_blocks)

164
gguf-py/gguf/vocab.py Normal file
View file

@ -0,0 +1,164 @@
from __future__ import annotations
import json
import os
import sys
from pathlib import Path
from typing import Any, Callable
from .gguf_writer import GGUFWriter
class SpecialVocab:
merges: list[str]
add_special_token: dict[str, bool]
special_token_ids: dict[str, int]
def __init__(
self, path: str | os.PathLike[str], load_merges: bool = False,
special_token_types: tuple[str, ...] | None = None,
n_vocab: int | None = None,
):
self.special_token_ids = {}
self.add_special_token = {}
self.n_vocab = n_vocab
self.load_merges = load_merges
self.merges = []
if special_token_types is not None:
self.special_token_types = special_token_types
else:
self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad')
self._load(Path(path))
def __repr__(self) -> str:
return '<SpecialVocab with {} merges, special tokens {}, add special tokens {}>'.format(
len(self.merges), self.special_token_ids or "unset", self.add_special_token or "unset",
)
def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None:
if self.merges:
if not quiet:
print(f'gguf: Adding {len(self.merges)} merge(s).')
gw.add_token_merges(self.merges)
elif self.load_merges:
print(
'gguf: WARNING: Adding merges requested but no merges found, output may be non-functional.',
file = sys.stderr,
)
for typ, tokid in self.special_token_ids.items():
id_handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None)
if id_handler is None:
print(
f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping',
file = sys.stderr,
)
continue
if not quiet:
print(f'gguf: Setting special token type {typ} to {tokid}')
id_handler(tokid)
for typ, value in self.add_special_token.items():
add_handler: Callable[[bool], None] | None = getattr(gw, f'add_add_{typ}_token', None)
if add_handler is None:
print(
f'gguf: WARNING: No handler for add_{typ}_token with value {value} - skipping',
file = sys.stderr,
)
continue
if not quiet:
print(f'gguf: Setting add_{typ}_token to {value}')
add_handler(value)
def _load(self, path: Path) -> None:
self._try_load_from_tokenizer_json(path)
self._try_load_from_config_json(path)
if self.load_merges and not self.merges:
self._try_load_merges_txt(path)
def _try_load_merges_txt(self, path: Path) -> bool:
merges_file = path / 'merges.txt'
if not merges_file.is_file():
return False
with open(merges_file, 'r') as fp:
first_line = next(fp, '').strip()
if not first_line.startswith('#'):
fp.seek(0)
line_num = 0
else:
line_num = 1
merges = []
for line in fp:
line_num += 1
line = line.strip()
if not line:
continue
parts = line.split(None, 3)
if len(parts) != 2:
print(
f'gguf: WARNING: {merges_file.name}: Line {line_num}: Entry malformed, ignoring',
file = sys.stderr,
)
continue
merges.append(f'{parts[0]} {parts[1]}')
self.merges = merges
return True
def _set_special_token(self, typ: str, tid: Any) -> None:
if not isinstance(tid, int) or tid < 0:
return
if self.n_vocab is None or tid < self.n_vocab:
if typ in self.special_token_ids:
return
self.special_token_ids[typ] = tid
return
print(
f'gguf: WARNING: Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping',
file = sys.stderr,
)
def _try_load_from_tokenizer_json(self, path: Path) -> bool:
tokenizer_file = path / 'tokenizer.json'
if not tokenizer_file.is_file():
return False
with open(tokenizer_file, encoding = 'utf-8') as f:
tokenizer = json.load(f)
if self.load_merges:
merges = tokenizer.get('model', {}).get('merges')
if isinstance(merges, list) and merges and isinstance(merges[0], str):
self.merges = merges
tokenizer_config_file = path / 'tokenizer_config.json'
added_tokens = tokenizer.get('added_tokens')
if added_tokens is None or not tokenizer_config_file.is_file():
return True
with open(tokenizer_config_file, encoding = 'utf-8') as f:
tokenizer_config = json.load(f)
for typ in self.special_token_types:
add_entry = tokenizer_config.get(f'add_{typ}_token')
if isinstance(add_entry, bool):
self.add_special_token[typ] = add_entry
entry = tokenizer_config.get(f'{typ}_token')
if isinstance(entry, str):
tc_content = entry
elif isinstance(entry, dict):
entry_content = entry.get('content')
if not isinstance(entry_content, str):
continue
tc_content = entry_content
else:
continue
# We only need the first match here.
maybe_token_id = next(
(atok.get('id') for atok in added_tokens if atok.get('content') == tc_content),
None,
)
self._set_special_token(typ, maybe_token_id)
return True
def _try_load_from_config_json(self, path: Path) -> bool:
config_file = path / 'config.json'
if not config_file.is_file():
return False
with open(config_file, encoding = 'utf-8') as f:
config = json.load(f)
for typ in self.special_token_types:
self._set_special_token(typ, config.get(f'{typ}_token_id'))
return True

10
gguf-py/pyproject.toml
View file

@ -1,11 +1,12 @@
[tool.poetry]
name = "gguf"
version = "0.4.6"
description = "Write ML models in GGUF for GGML"
version = "0.5.2"
description = "Read and write ML models in GGUF for GGML"
authors = ["GGML <ggml@ggml.ai>"]
packages = [
{include = "gguf"},
{include = "gguf/py.typed"},
{include = "scripts"},
]
readme = "README.md"
homepage = "https://ggml.ai"
@ -27,3 +28,8 @@ pytest = "^5.2"
[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"
[tool.poetry.scripts]
gguf-convert-endian = "scripts:gguf_convert_endian_entrypoint"
gguf-dump = "scripts:gguf_dump_entrypoint"
gguf-set-metadata = "scripts:gguf_set_metadata_entrypoint"

12
gguf-py/scripts/__init__.py Normal file
View file

@ -0,0 +1,12 @@
import os
from importlib import import_module
os.environ["NO_LOCAL_GGUF"] = "TRUE"
gguf_convert_endian_entrypoint = import_module("scripts.gguf-convert-endian").main
gguf_dump_entrypoint = import_module("scripts.gguf-dump").main
gguf_set_metadata_entrypoint = import_module("scripts.gguf-set-metadata").main
del import_module, os

112
gguf-py/scripts/gguf-convert-endian.py Executable file
View file

@ -0,0 +1,112 @@
#!/usr/bin/env python3
from __future__ import annotations
import argparse
import os
import sys
from pathlib import Path
import numpy as np
# Necessary to load the local gguf package
if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
sys.path.insert(0, str(Path(__file__).parent.parent))
import gguf
def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None:
if np.uint32(1) == np.uint32(1).newbyteorder("<"):
# Host is little endian
host_endian = "little"
swapped_endian = "big"
else:
# Sorry PDP or other weird systems that don't use BE or LE.
host_endian = "big"
swapped_endian = "little"
if reader.byte_order == "S":
file_endian = swapped_endian
else:
file_endian = host_endian
order = host_endian if args.order == "native" else args.order
print(f"* Host is {host_endian.upper()} endian, GGUF file seems to be {file_endian.upper()} endian")
if file_endian == order:
print(f"* File is already {order.upper()} endian. Nothing to do.")
sys.exit(0)
print("* Checking tensors for conversion compatibility")
for tensor in reader.tensors:
if tensor.tensor_type not in (
gguf.GGMLQuantizationType.F32,
gguf.GGMLQuantizationType.F16,
gguf.GGMLQuantizationType.Q8_0,
):
raise ValueError(f"Cannot handle type {tensor.tensor_type.name} for tensor {repr(tensor.name)}")
print(f"* Preparing to convert from {file_endian.upper()} to {order.upper()}")
if args.dry_run:
return
print("\n*** Warning *** Warning *** Warning **")
print("* This conversion process may damage the file. Ensure you have a backup.")
if order != host_endian:
print("* Requested endian differs from host, you will not be able to load the model on this machine.")
print("* The file will be modified immediately, so if conversion fails or is interrupted")
print("* the file will be corrupted. Enter exactly YES if you are positive you want to proceed:")
response = input("YES, I am sure> ")
if response != "YES":
print("You didn't enter YES. Okay then, see ya!")
sys.exit(0)
print(f"\n* Converting fields ({len(reader.fields)})")
for idx, field in enumerate(reader.fields.values()):
print(f"- {idx:4}: Converting field {repr(field.name)}, part count: {len(field.parts)}")
for part in field.parts:
part.byteswap(inplace=True)
print(f"\n* Converting tensors ({len(reader.tensors)})")
for idx, tensor in enumerate(reader.tensors):
print(
f" - {idx:4}: Converting tensor {repr(tensor.name)}, type={tensor.tensor_type.name}, "
f"elements={tensor.n_elements}... ",
end="",
)
tensor_type = tensor.tensor_type
for part in tensor.field.parts:
part.byteswap(inplace=True)
if tensor_type != gguf.GGMLQuantizationType.Q8_0:
tensor.data.byteswap(inplace=True)
print()
continue
# A Q8_0 block consists of a f16 delta followed by 32 int8 quants, so 34 bytes
block_size = 34
n_blocks = len(tensor.data) // block_size
for block_num in range(n_blocks):
block_offs = block_num * block_size
# I know I said f16, but it doesn't matter here - any simple 16 bit type works.
delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16)
delta.byteswap(inplace=True)
if block_num % 100000 == 0:
print(f"[{(n_blocks - block_num) // 1000}K]", end="")
sys.stdout.flush()
print()
print("* Completion")
def main() -> None:
parser = argparse.ArgumentParser(description="Convert GGUF file byte order")
parser.add_argument(
"model", type=str,
help="GGUF format model filename",
)
parser.add_argument(
"order", type=str, choices=['big', 'little', 'native'],
help="Requested byte order",
)
parser.add_argument(
"--dry-run", action="store_true",
help="Don't actually change anything",
)
args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
print(f'* Loading: {args.model}')
reader = gguf.GGUFReader(args.model, 'r' if args.dry_run else 'r+')
convert_byteorder(reader, args)
if __name__ == "__main__":
main()

116
gguf-py/scripts/gguf-dump.py Executable file
View file

@ -0,0 +1,116 @@
#!/usr/bin/env python3
from __future__ import annotations
import argparse
import os
import sys
from pathlib import Path
from typing import Any
import numpy as np
# Necessary to load the local gguf package
if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
sys.path.insert(0, str(Path(__file__).parent.parent))
from gguf import GGUFReader, GGUFValueType # noqa: E402
def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]:
host_endian = 'LITTLE' if np.uint32(1) == np.uint32(1).newbyteorder("<") else 'BIG'
if reader.byte_order == 'S':
file_endian = 'BIG' if host_endian == 'LITTLE' else 'LITTLE'
else:
file_endian = host_endian
return (host_endian, file_endian)
# For more information about what field.parts and field.data represent,
# please see the comments in the modify_gguf.py example.
def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
host_endian, file_endian = get_file_host_endian(reader)
print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.')
print(f'\n* Dumping {len(reader.fields)} key/value pair(s)')
for n, field in enumerate(reader.fields.values(), 1):
if not field.types:
pretty_type = 'N/A'
elif field.types[0] == GGUFValueType.ARRAY:
nest_count = len(field.types) - 1
pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count
else:
pretty_type = str(field.types[-1].name)
print(f' {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}', end = '')
if len(field.types) == 1:
curr_type = field.types[0]
if curr_type == GGUFValueType.STRING:
print(' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf8')[:60])), end = '')
elif field.types[0] in reader.gguf_scalar_to_np:
print(' = {0}'.format(field.parts[-1][0]), end = '')
print()
if args.no_tensors:
return
print(f'\n* Dumping {len(reader.tensors)} tensor(s)')
for n, tensor in enumerate(reader.tensors, 1):
prettydims = ', '.join('{0:5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape)))
print(f' {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}')
def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None:
import json
host_endian, file_endian = get_file_host_endian(reader)
metadata: dict[str, Any] = {}
tensors: dict[str, Any] = {}
result = {
"filename": args.model,
"endian": file_endian,
"metadata": metadata,
"tensors": tensors,
}
for idx, field in enumerate(reader.fields.values()):
curr: dict[str, Any] = {
"index": idx,
"type": field.types[0].name if field.types else 'UNKNOWN',
"offset": field.offset,
}
metadata[field.name] = curr
if field.types[:1] == [GGUFValueType.ARRAY]:
curr["array_types"] = [t.name for t in field.types][1:]
if not args.json_array:
continue
itype = field.types[-1]
if itype == GGUFValueType.STRING:
curr["value"] = [str(bytes(field.parts[idx]), encoding="utf-8") for idx in field.data]
else:
curr["value"] = [pv for idx in field.data for pv in field.parts[idx].tolist()]
elif field.types[0] == GGUFValueType.STRING:
curr["value"] = str(bytes(field.parts[-1]), encoding="utf-8")
else:
curr["value"] = field.parts[-1].tolist()[0]
for idx, tensor in enumerate(reader.tensors):
tensors[tensor.name] = {
"index": idx,
"shape": tensor.shape.tolist(),
"type": tensor.tensor_type.name,
"offset": tensor.field.offset,
}
json.dump(result, sys.stdout)
def main() -> None:
parser = argparse.ArgumentParser(description="Dump GGUF file metadata")
parser.add_argument("model", type=str, help="GGUF format model filename")
parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata")
parser.add_argument("--json", action="store_true", help="Produce JSON output")
parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)")
args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
if not args.json:
print(f'* Loading: {args.model}')
reader = GGUFReader(args.model, 'r')
if args.json:
dump_metadata_json(reader, args)
else:
dump_metadata(reader, args)
if __name__ == '__main__':
main()

90
gguf-py/scripts/gguf-set-metadata.py Executable file
View file

@ -0,0 +1,90 @@
#!/usr/bin/env python3
import argparse
import os
import sys
from pathlib import Path
# Necessary to load the local gguf package
if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists():
sys.path.insert(0, str(Path(__file__).parent.parent))
from gguf import GGUFReader # noqa: E402
def minimal_example(filename: str) -> None:
reader = GGUFReader(filename, 'r+')
field = reader.fields['tokenizer.ggml.bos_token_id']
if field is None:
return
part_index = field.data[0]
field.parts[part_index][0] = 2 # Set tokenizer.ggml.bos_token_id to 2
#
# So what's this field.data thing? It's helpful because field.parts contains
# _every_ part of the GGUF field. For example, tokenizer.ggml.bos_token_id consists
# of:
#
# Part index 0: Key length (27)
# Part index 1: Key data ("tokenizer.ggml.bos_token_id")
# Part index 2: Field type (4, the id for GGUFValueType.UINT32)
# Part index 3: Field value
#
# Note also that each part is an NDArray slice, so even a part that
# is only a single value like the key length will be a NDArray of
# the key length type (numpy.uint32).
#
# The .data attribute in the Field is a list of relevant part indexes
# and doesn't contain internal GGUF details like the key length part.
# In this case, .data will be [3] - just the part index of the
# field value itself.
def set_metadata(reader: GGUFReader, args: argparse.Namespace) -> None:
field = reader.get_field(args.key)
if field is None:
print(f'! Field {repr(args.key)} not found', file = sys.stderr)
sys.exit(1)
# Note that field.types is a list of types. This is because the GGUF
# format supports arrays. For example, an array of UINT32 would
# look like [GGUFValueType.ARRAY, GGUFValueType.UINT32]
handler = reader.gguf_scalar_to_np.get(field.types[0]) if field.types else None
if handler is None:
print(
f'! This tool only supports changing simple values, {repr(args.key)} has unsupported type {field.types}',
file = sys.stderr,
)
sys.exit(1)
current_value = field.parts[field.data[0]][0]
new_value = handler(args.value)
print(f'* Preparing to change field {repr(args.key)} from {current_value} to {new_value}')
if current_value == new_value:
print(f'- Key {repr(args.key)} already set to requested value {current_value}')
sys.exit(0)
if args.dry_run:
sys.exit(0)
if not args.force:
print('*** Warning *** Warning *** Warning **')
print('* Changing fields in a GGUF file can make it unusable. Proceed at your own risk.')
print('* Enter exactly YES if you are positive you want to proceed:')
response = input('YES, I am sure> ')
if response != 'YES':
print("You didn't enter YES. Okay then, see ya!")
sys.exit(0)
field.parts[field.data[0]][0] = new_value
print('* Field changed. Successful completion.')
def main() -> None:
parser = argparse.ArgumentParser(description="Set a simple value in GGUF file metadata")
parser.add_argument("model", type=str, help="GGUF format model filename")
parser.add_argument("key", type=str, help="Metadata key to set")
parser.add_argument("value", type=str, help="Metadata value to set")
parser.add_argument("--dry-run", action="store_true", help="Don't actually change anything")
parser.add_argument("--force", action="store_true", help="Change the field without confirmation")
args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"])
print(f'* Loading: {args.model}')
reader = GGUFReader(args.model, 'r' if args.dry_run else 'r+')
set_metadata(reader, args)
if __name__ == '__main__':
main()

4
gguf-py/tests/test_gguf.py
View file

@ -1,7 +1,7 @@
import gguf
import gguf # noqa: F401
# TODO: add tests
def test_write_gguf():
def test_write_gguf() -> None:
pass

331
llama.cpp
View file

@ -92,6 +92,8 @@
#define LLAMA_ATTRIBUTE_FORMAT(...)
#endif
#define LLAMA_MAX_NODES 4096
//
// logging
//
@ -191,6 +193,7 @@ enum llm_arch {
LLM_ARCH_PERSIMMON,
LLM_ARCH_REFACT,
LLM_ARCH_BLOOM,
LLM_ARCH_STABLELM,
LLM_ARCH_UNKNOWN,
};
@ -206,6 +209,7 @@ static std::map<llm_arch, std::string> LLM_ARCH_NAMES = {
{ LLM_ARCH_PERSIMMON, "persimmon" },
{ LLM_ARCH_REFACT, "refact" },
{ LLM_ARCH_BLOOM, "bloom" },
{ LLM_ARCH_STABLELM, "stablelm" },
};
enum llm_kv {
@ -494,6 +498,25 @@ static std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES =
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
},
},
{
LLM_ARCH_STABLELM,
{
{ LLM_TENSOR_TOKEN_EMBD, "token_embd" },
{ LLM_TENSOR_OUTPUT_NORM, "output_norm" },
{ LLM_TENSOR_OUTPUT, "output" },
{ LLM_TENSOR_ROPE_FREQS, "rope_freqs" },
{ LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" },
{ LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" },
{ LLM_TENSOR_ATTN_K, "blk.%d.attn_k" },
{ LLM_TENSOR_ATTN_V, "blk.%d.attn_v" },
{ LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" },
{ LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" },
{ LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" },
{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
},
},
{
LLM_ARCH_UNKNOWN,
{
@ -2225,6 +2248,16 @@ static void llm_load_hparams(
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
case LLM_ARCH_STABLELM:
{
GGUF_GET_KEY(ctx, hparams.f_norm_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, kv(LLM_KV_ATTENTION_LAYERNORM_EPS));
switch (hparams.n_layer) {
case 32: model.type = e_model::MODEL_3B; break;
default: model.type = e_model::MODEL_UNKNOWN;
}
} break;
default: (void)0;
}
@ -2907,6 +2940,13 @@ static void llm_load_tensors(
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
#ifdef GGML_USE_CUBLAS
if (n_gpu_layers > int(n_layer + 1)) {
LLAMA_LOG_ERROR("%s: CUDA backend missing Persimmon CUDA ops, can offload at most %ld layers. See: https://github.com/ggerganov/llama.cpp/issues/4038\n",
__func__, n_layer + 1);
throw std::runtime_error("Persimmon CUDA offload failed");
}
#endif
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
@ -3108,6 +3148,81 @@ static void llm_load_tensors(
}
}
} break;
case LLM_ARCH_STABLELM:
{
model.tok_embd = ml.create_tensor(ctx, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, GGML_BACKEND_CPU);
// output
{
ggml_backend_type backend_norm;
ggml_backend_type backend_output;
if (n_gpu_layers > int(n_layer)) {
// norm is not performance relevant on its own but keeping it in VRAM reduces data copying
// on Windows however this is detrimental unless everything is on the GPU
#ifndef _WIN32
backend_norm = llama_backend_offload;
#else
backend_norm = n_gpu_layers <= (int) n_layer + 2 ? GGML_BACKEND_CPU : llama_backend_offload;
#endif // _WIN32
backend_output = llama_backend_offload_split;
} else {
backend_norm = GGML_BACKEND_CPU;
backend_output = GGML_BACKEND_CPU;
}
model.output_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, backend_norm);
model.output_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, backend_norm);
model.output = ml.create_tensor(ctx, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, backend_output);
if (backend_norm == GGML_BACKEND_GPU) {
vram_weights += ggml_nbytes(model.output_norm);
}
if (backend_output == GGML_BACKEND_GPU_SPLIT) {
vram_weights += ggml_nbytes(model.output);
}
}
const uint32_t n_ff = hparams.n_ff;
const int i_gpu_start = n_layer - n_gpu_layers;
model.layers.resize(n_layer);
for (uint32_t i = 0; i < n_layer; ++i) {
/*
llama_model_loader: - tensor 4: blk.0.attn_output.weight f16 [ 2560, 2560, 1, 1 ]
*/
const ggml_backend_type backend = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload; // NOLINT
const ggml_backend_type backend_split = int(i) < i_gpu_start ? GGML_BACKEND_CPU : llama_backend_offload_split; // NOLINT
auto & layer = model.layers[i];
layer.attn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, backend);
layer.attn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, backend);
layer.wq = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}, backend_split);
layer.wk = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wv = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, backend_split);
layer.wo = ml.create_tensor(ctx, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}, backend_split);
layer.ffn_norm = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, backend);
layer.ffn_norm_b = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, backend);
layer.ffn_gate = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, backend_split);
layer.ffn_down = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, backend_split);
layer.ffn_up = ml.create_tensor(ctx, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, backend_split);
if (backend == GGML_BACKEND_GPU) {
vram_weights +=
ggml_nbytes(layer.attn_norm) + ggml_nbytes(layer.wq) + ggml_nbytes(layer.wk) +
ggml_nbytes(layer.wv) + ggml_nbytes(layer.wo) + ggml_nbytes(layer.ffn_norm) +
ggml_nbytes(layer.ffn_gate) + ggml_nbytes(layer.ffn_down) + ggml_nbytes(layer.ffn_up);
}
}
} break;
default:
throw std::runtime_error("unknown architecture");
}
@ -3641,7 +3756,7 @@ struct llm_build_context {
}
struct ggml_cgraph * build_llama() {
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
GGML_ASSERT(n_embd_head == hparams.n_rot);
@ -3753,7 +3868,7 @@ struct llm_build_context {
}
struct ggml_cgraph * build_baichuan() {
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
@ -3873,7 +3988,7 @@ struct llm_build_context {
}
struct ggml_cgraph * build_falcon() {
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
@ -3995,7 +4110,7 @@ struct llm_build_context {
}
struct ggml_cgraph * build_starcoder() {
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
struct ggml_tensor * cur;
struct ggml_tensor * pos;
@ -4094,7 +4209,7 @@ struct llm_build_context {
}
struct ggml_cgraph * build_persimmon() {
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
const int64_t n_rot = n_embd_head / 2;
@ -4304,7 +4419,7 @@ struct llm_build_context {
}
struct ggml_cgraph * build_refact() {
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
@ -4395,7 +4510,7 @@ struct llm_build_context {
}
struct ggml_cgraph * build_bloom() {
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
@ -4489,7 +4604,7 @@ struct llm_build_context {
}
struct ggml_cgraph * build_mpt() {
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
@ -4586,6 +4701,177 @@ struct llm_build_context {
return gf;
}
struct ggml_cgraph * build_stablelm() {
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
struct ggml_tensor * cur;
struct ggml_tensor * inpL;
inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb);
cb(inpL, "inp_embd", -1);
// inp_pos - contains the positions
struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
cb(inp_pos, "inp_pos", -1);
// KQ_scale
struct ggml_tensor * KQ_scale = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, 1);
cb(KQ_scale, "KQ_scale", -1);
// KQ_mask (mask for 1 head, it will be broadcasted to all heads)
struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1);
cb(KQ_mask, "KQ_mask", -1);
// shift the entire K-cache if needed
if (do_rope_shift) {
llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, hparams.n_rot, freq_base, freq_scale, cb);
}
for (int il = 0; il < n_layer; ++il) {
struct ggml_tensor * inpSA = inpL;
// norm
cur = llm_build_norm(ctx0, inpL, hparams,
model.layers[il].attn_norm,
model.layers[il].attn_norm_b,
LLM_NORM, cb, il);
cb(cur, "attn_norm", il);
// self-attention
{
// compute Q and K and RoPE them
struct ggml_tensor * tmpq = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
cb(tmpq, "tmpq", il);
struct ggml_tensor * tmpk = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
cb(tmpk, "tmpk", il);
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
cb(Vcur, "Vcur", il);
// RoPE the first n_rot of q/k, pass the other half, and concat.
struct ggml_tensor * qrot = ggml_cont(ctx0, ggml_view_3d(
ctx0, tmpq, hparams.n_rot, n_head, n_tokens,
ggml_element_size(tmpq) * n_embd_head,
ggml_element_size(tmpq) * n_embd_head * n_head,
0
));
cb(qrot, "qrot", il);
struct ggml_tensor * krot = ggml_cont(ctx0, ggml_view_3d(
ctx0, tmpk, hparams.n_rot, n_head, n_tokens,
ggml_element_size(tmpk) * n_embd_head,
ggml_element_size(tmpk) * n_embd_head * n_head_kv,
0
));
cb(krot, "krot", il);
// get the second half of tmpq, e.g tmpq[n_rot:, :, :]
struct ggml_tensor * qpass = ggml_view_3d(
ctx0, tmpq, (n_embd_head - hparams.n_rot), n_head, n_tokens,
ggml_element_size(tmpq) * n_embd_head,
ggml_element_size(tmpq) * n_embd_head * n_head,
ggml_element_size(tmpq) * hparams.n_rot
);
cb(qpass, "qpass", il);
struct ggml_tensor * kpass = ggml_view_3d(
ctx0, tmpk, (n_embd_head - hparams.n_rot), n_head_kv, n_tokens,
ggml_element_size(tmpk) * (n_embd_head),
ggml_element_size(tmpk) * (n_embd_head) * n_head_kv,
ggml_element_size(tmpk) * hparams.n_rot
);
cb(kpass, "kpass", il);
struct ggml_tensor * qrotated = ggml_rope_custom(
ctx0, qrot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(qrotated, "qrotated", il);
struct ggml_tensor * krotated = ggml_rope_custom(
ctx0, krot, inp_pos, hparams.n_rot, 2, 0, n_orig_ctx,
freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
);
cb(krotated, "krotated", il);
// ggml currently only supports concatenation on dim=2
// so we need to permute qrot, qpass, concat, then permute back.
qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3));
cb(qrotated, "qrotated", il);
krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3));
cb(krotated, "krotated", il);
qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3));
cb(qpass, "qpass", il);
kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3));
cb(kpass, "kpass", il);
struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass);
cb(Qcur, "Qcur", il);
struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass);
cb(Kcur, "Kcur", il);
struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3));
cb(Q, "Q", il);
Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3));
cb(Kcur, "Kcur", il);
llm_build_kv_store(ctx0, hparams, kv_self, gf, Kcur, Vcur, n_ctx, n_tokens, kv_head, cb, il);
cur = llm_build_kqv(ctx0, hparams, kv_self,
model.layers[il].wo, NULL,
Q, KQ_scale, KQ_mask, n_ctx, n_tokens, n_kv, -1.0f, cb, il);
cb(cur, "kqv_out", il);
}
struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
cb(ffn_inp, "ffn_inp", il);
// feed-forward network
{
cur = llm_build_norm(ctx0, ffn_inp, hparams,
model.layers[il].ffn_norm,
model.layers[il].ffn_norm_b,
LLM_NORM, cb, il);
cb(cur, "ffn_norm", il);
cur = llm_build_ffn(ctx0, cur,
model.layers[il].ffn_up, NULL,
model.layers[il].ffn_gate, NULL,
model.layers[il].ffn_down, NULL,
LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
cb(cur, "ffn_out", il);
}
cur = ggml_add(ctx0, cur, ffn_inp);
cb(cur, "l_out", il);
// input for next layer
inpL = cur;
}
cur = inpL;
cur = llm_build_norm(ctx0, cur, hparams,
model.output_norm,
model.output_norm_b,
LLM_NORM, cb, -1);
cb(cur, "result_norm", -1);
// lm_head
cur = ggml_mul_mat(ctx0, model.output, cur);
cb(cur, "result_output", -1);
ggml_build_forward_expand(gf, cur);
return gf;
}
};
//
@ -5055,6 +5341,10 @@ static struct ggml_cgraph * llama_build_graph(
{
result = llm.build_mpt();
} break;
case LLM_ARCH_STABLELM:
{
result = llm.build_stablelm();
} break;
default:
GGML_ASSERT(false);
}
@ -5232,7 +5522,8 @@ static int llama_decode_internal(
model.arch == LLM_ARCH_FALCON ||
model.arch == LLM_ARCH_REFACT ||
model.arch == LLM_ARCH_MPT ||
model.arch == LLM_ARCH_STARCODER;
model.arch == LLM_ARCH_STARCODER ||
model.arch == LLM_ARCH_STABLELM;
const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 3;
if (ggml_cpu_has_cublas() && full_offload_supported && fully_offloaded) {
@ -8467,7 +8758,7 @@ struct llama_context * llama_new_context_with_model(
{
static const size_t tensor_alignment = 32;
// the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
ctx->buf_compute.resize(ggml_tensor_overhead()*LLAMA_MAX_NODES + ggml_graph_overhead());
// create measure allocator
ctx->alloc = ggml_allocr_new_measure(tensor_alignment);
@ -8856,8 +9147,8 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
if (kv_buf_size) {
const size_t elt_size = ggml_element_size(kv_self.k);
ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true });
ggml_cgraph gf{};
ggml_context * cpy_ctx = ggml_init({ 6*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true });
ggml_cgraph * gf = ggml_new_graph(cpy_ctx);
ggml_tensor * kout3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer);
std::vector<uint8_t> kout3d_data(ggml_nbytes(kout3d), 0);
@ -8875,9 +9166,9 @@ static void llama_copy_state_data_internal(struct llama_context * ctx, llama_dat
kv_head, n_embd, n_layer,
elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, k3d, kout3d));
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, v3d, vout3d));
ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1);
ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, k3d, kout3d));
ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, v3d, vout3d));
ggml_graph_compute_helper(ctx->work_buffer, gf, /*n_threads*/ 1);
ggml_free(cpy_ctx);
@ -8984,8 +9275,8 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
const size_t elt_size = ggml_element_size(kv_self.k);
ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true });
ggml_cgraph gf{};
ggml_context * cpy_ctx = ggml_init({ 6*ggml_tensor_overhead() + ggml_graph_overhead(), NULL, /* no_alloc */ true });
ggml_cgraph * gf = ggml_new_graph(cpy_ctx);
ggml_tensor * kin3d = ggml_new_tensor_3d(cpy_ctx, kv_self.k->type, n_embd, kv_head, n_layer);
kin3d->data = (void *) inp;
@ -9003,9 +9294,9 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) {
kv_head, n_embd, n_layer,
elt_size*n_ctx, elt_size*n_ctx*n_embd, 0);
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, kin3d, k3d));
ggml_build_forward_expand(&gf, ggml_cpy(cpy_ctx, vin3d, v3d));
ggml_graph_compute_helper(ctx->work_buffer, &gf, /*n_threads*/ 1);
ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, kin3d, k3d));
ggml_build_forward_expand(gf, ggml_cpy(cpy_ctx, vin3d, v3d));
ggml_graph_compute_helper(ctx->work_buffer, gf, /*n_threads*/ 1);
ggml_free(cpy_ctx);
}

BIN
models/ggml-vocab-stablelm-3b-4e1t.gguf Normal file
View file

Binary file not shown.

6
otherarch/llama_v3.cpp
View file

@ -3457,6 +3457,7 @@ struct llama_v3_context * llama_v3_new_context_with_model(
#ifdef LLAMA_V3_USE_ALLOCATOR
{
static const size_t tensor_alignment = 32;
static const size_t GGML_MAX_NODES = 4096;
// the compute buffer is used to store the tensor and graph structs, while the allocator buffer is used for the tensor data
ctx->buf_compute.resize(ggml_tensor_overhead()*GGML_MAX_NODES + ggml_graph_overhead());
@ -3849,9 +3850,10 @@ int llama_v3_apply_lora_from_file_internal(const struct llama_v3_model & model,
ggml_set_name(r, "r_cpy");
}
struct ggml_cgraph gf = ggml_build_forward(r);
struct ggml_cgraph * gf = ggml_new_graph(lora_ctx);
ggml_build_forward_expand(gf, r);
llv3_graph_compute_helper(work_buffer, &gf, n_threads);
llv3_graph_compute_helper(work_buffer, gf, n_threads);
// we won't need these tensors again, reset the context to save memory
ggml_free(lora_ctx);